Drug Information: A Guide for Pharmacists (Malone, Drug Information) [5 ed.] 007180434X, 9780071804349

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drug information A GUIDE FOR PHARMACISTS

Notice Medicine is an ever-changing science. As new research and clinical experience broaden our knowledge, changes in treatment and drug therapy are required. The authors and the publisher of this work have checked with sources believed to be reliable in their efforts to provide information that is complete and generally in accord with the standards accepted at the time of publication. However, in view of the possibility of human error or changes in medical sciences, neither the authors nor the publisher nor any other party who has been involved in the preparation or publication of this work warrants that the information contained herein is in every respect accurate or complete, and they disclaim all responsibility for any errors or omissions or for the results obtained from use of the information contained in this work. Readers are encouraged to confirm the information contained herein with other sources. For example and in particular, readers are advised to check the product information sheet included in the package of each drug they plan to administer to be certain that the information contained in this work is accurate and that changes have not been made in the recommended dose or in the contraindications for administration. This recommendation is of particular importance in connection with new or infrequently used drugs.

drug information A GUIDE FOR PHARMACISTS fifth edition

Editors

Patrick M. Malone, PharmD, FASHP Associate Dean of Internal Affairs and Professor of Pharmacy Practice College of Pharmacy The University of Findlay Findlay, Ohio

Karen L. Kier, PhD, MSc, RPh, BCPS, BCACP Professor of Clinical Pharmacy Director of Assessment Raabe College of Pharmacy Ohio Northern University Ada, Ohio

John E. Stanovich, RPh Assistant Professor of Pharmacy Practice Assistant Dean for External Programs College of Pharmacy The University of Findlay Findlay, Ohio

Meghan J. Malone, PharmD, BCPS, CACP Clinical Pharmacist Geisinger Health System Danville, Pennsylvania

New York

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Athens

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London

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Sydney

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Madrid Toronto

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Milan

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Contents Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xix Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxv Chapter One. Introduction to the Concept of Drug Information. . . . . . . . . . . . . . . . . . . . . . . . . 1 Mary Lea Gora-Harper and J. Russell May Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 The Beginning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 The Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Opportunities in Specialty Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Summary and Direction for the Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Chapter Two. Formulating Effective Responses and Recommendations: A Structured Approach . . . . . . . 35 Karim Anton Calis and Amy Heck Sheehan Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Case Study 2–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Case Study 2–2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Case Study 2–3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 Case Study 2–4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

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Chapter Three. Drug Information Resources . . . . . . . . . . . . . . . . 59 Kelly M. Shields and Elaine Blythe Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Tertiary Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Case Study 3–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Case Study 3–2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Case Study 3–3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 Case Study 3–4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Secondary Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 Case Study 3–5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 Primary Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Alternative Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 Case Study 3–6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 Consumer Health Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Case Study 3–7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

Chapter Four. Drug Literature Evaluation I: Controlled Clinical Trial Evaluation . . . . . . . . . . . 105 Michael G. Kendrach, Maisha Kelly Freeman, and Peter J. Hughes Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 Biomedical/Pharmacy Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 Approach to Evaluating Research Studies (True Experiments) . . . . . . . . . . . 111 Case Study 4–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 Case Study 4–2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 Case Study 4–3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170

Chapter Five. Literature Evaluation II: Beyond the Basics . . . . . . 187 Patrick J. Bryant, Cydney E. McQueen, and Elizabeth A. Van Dyke Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188 Beyond the Basic Controlled Trial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191

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Case Study 5–1: Noninferiority Trial Design . . . . . . . . . . . . . . . . . . . . . . . . . . 198 Case Study 5–2: Noninferiority Trial Design . . . . . . . . . . . . . . . . . . . . . . . . . . 200 Observational Study Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 Case Study 5–3: Cohort Study Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Reports Without Control Group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226 Survey Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 Postmarketing Surveillance Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Review Articles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234 Case Study 5–4: Meta-Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244 Practice Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Health Outcomes Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 Dietary Supplement Medical Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252 Case Study 5–5: Trials Testing Natural Products . . . . . . . . . . . . . . . . . . . . . . . 256 Getting to a Clinical Decision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263

Chapter Six. Pharmacoeconomics . . . . . . . . . . . . . . . . . . . . . . . 273 James P. Wilson and Karen L. Rascati Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274 Pharmacoeconomics: What Is It and Why Do It? . . . . . . . . . . . . . . . . . . . . . . 274 Relationships of Pharmacoeconomics to Outcomes Research . . . . . . . . . . . . 275 Models of Pharmacoeconomic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 Assessment of Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 Assessment of Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278 Performing an Economic Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 What Is Decision Analysis? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292 Steps in Reviewing Published Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296 Case Study 6–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 Selected Pharmacoeconomic Web Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303

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Chapter Seven. Evidence-Based Clinical Practice Guidelines . . . . 311 Kevin G. Moores and Vicki R. Kee Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312 Evidence-Based Medicine and Clinical Practice Guidelines . . . . . . . . . . . . . . 314 Guideline Development Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316 Case Study 7–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320 Case Study 7–2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 Case Study 7–3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 Guideline Evaluation Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 Implementation of Clinical Practice Guidelines . . . . . . . . . . . . . . . . . . . . . . . . 336 Case Study 7–4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340 Sources of Clinical Practice Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343

Chapter Eight. The Application of Statistical Analysis in the Biomedical Sciences . . . . . . . . . . . . . . . . . 351 Ryan W. Walters Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352 Populations and Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 Variables and the Measurement of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357 Common Probability Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 Epidemiological Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 370 Types of Study Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374 Case Study 8–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376 The Design and Analysis of Clinical Trials . . . . . . . . . . . . . . . . . . . . . . . . . . . 377 Statistical Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 Selecting the Appropriate Statistical Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394 Case Study 8–2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 Statistical Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 450 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451

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Chapter Nine. Professional Writing . . . . . . . . . . . . . . . . . . . . . . 459 Patrick M. Malone and Meghan J. Malone Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460 Steps in Writing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461 Case Study 9–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 477 Specific Documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 477 Case Study 9–2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485 Case Study 9–3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493

Chapter Ten. Legal Aspects of Drug Information Practice . . . . . . 503 Martha M. Rumore Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 504 Tort Law . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505 Case Study 10–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507 Case Study 10–2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508 Defenses to Negligence and Malpractice Protection . . . . . . . . . . . . . . . . . . . . 518 Labeling and Advertising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523 Liability Concerns for Web 2.0 Information . . . . . . . . . . . . . . . . . . . . . . . . . . . 531 Intellectual Property Rights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 537 Case Study 10–3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 540 Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 546 Case Study 10–4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 548 Industry Support for Educational Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . 550 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 554 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 555

Chapter Eleven. Ethical Aspects of Drug Information Practice . . . 569 Linda K. Ohri What Is Ethics and What Is Not . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 570 Ethical Dilemmas When Providing Drug Information . . . . . . . . . . . . . . . . . . 572 Basics of Ethics Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573 Example Case 11–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 582

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Example Case 11–2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587 Case Study 11–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 590 Case Study 11–2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 591 Resources for Use by Professionals Seeking to Learn More about Medical Ethics, as Applied to Issues Involving Provision of Drug Information . . . . . . 592 Structures That Support Ethical Decision Making . . . . . . . . . . . . . . . . . . . . . 595 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 597 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 598

Chapter Twelve. Pharmacy and Therapeutics Committee . . . . . . . 607 Patrick M. Malone, Nancy L. Fagan, Mark A. Malesker, and Paul J. Nelson Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 608 Organizational Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 609 Pharmacy Support of the P&T Committee . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615 Case Study 12–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 624 Case Study 12–2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 639 Clinical Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 642 Standard Order Set Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 642 Credentialing and Privileges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 644 Quality Improvement Within the P&T Committee— Internal Audit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 645 Case Study 12–3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 649 Communication Within an Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 650 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 652 Discussion Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 652 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653 Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 655

Chapter Thirteen. Drug Evaluation Monographs . . . . . . . . . . . . . 669 Patrick M. Malone, Nancy L. Fagan, Mark A. Malesker, and Paul J. Nelson Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 670 Drug Evaluation Monograph Sections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 673 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 693 Case Study 13–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 693 Case Study 13–2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 693

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Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 694 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 696

Chapter Fourteen. Quality Improvement and the Medication Use System . . . . . . . . . . . . . . . . . . . . . . . . . 703 David P. Nau Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 704 The Changing Environment for Health Care Quality . . . . . . . . . . . . . . . . . . . 704 Purpose of Measuring Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 705 Quality Improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 706 Case Study 14–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 717 Case Study 14–2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 724 Case Study 14–3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 726 Quality in Drug Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 732 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 734 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 735

Chapter Fifteen. Medication Misadventures I: Adverse Drug Reactions . . . . . . . . . . . . . . . . . . 741 Zara Risoldi Cochrane, Darren Hein, and Philip J. Gregory Introduction to Adverse Drug Reactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 742 Causality and Probability of Adverse Drug Reactions . . . . . . . . . . . . . . . . . . . 745 Case Study 15–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 749 Classification of Adverse Drug Reactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 751 Implementing a Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 752 Reporting Adverse Drug Reactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 758 Case Study 15–2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 764 Future Approaches to Pharmacovigilance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 764 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 766 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 767

Chapter Sixteen. Medication Misadventures II: Medication and Patient Safety . . . . . . . . . . . . . 777 Kathryn A. Crea Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 778

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Definitions: Medication Errors, Adverse Drug Events, and Adverse Drug Reactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 779 The Impact of Errors on Patients and Health Care Systems . . . . . . . . . . . . . . 782 Identification and Reporting of Medication Errors and Adverse Drug Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 784 Classification of Error Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 787 Classifying Patient Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 791 National Reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 792 Managing an Event Reporting System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 793 Types of Safety Event Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 795 Case Study 16–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 797 To Err Is Human . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 798 System Error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 800 Case Study 16–2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 802 A Just Culture—Not Shame and Blame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 802 Case Study 16–3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 806 Risk Factors for Errors and Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 806 Health Professions Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 810 Best Practices for Error Prevention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 811 Other Principles of Error Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 816 Putting It All Together . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 819 Case Study 16–4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 819 Conclusion: Safety as a Priority . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 820 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 821

Chapter Seventeen. Investigational Drugs . . . . . . . . . . . . . . . . . 829 Bambi Grilley Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 831 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 832 History of Drug Development Regulation in the United States . . . . . . . . . . . 834 The Drug Approval Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 837 Case Study 17–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 844 Case Study 17–2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 848 The Orphan Drug Act . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 848

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The Institutional Review Board . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 849 Case Study 17–3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 851 Role of the Health Care Professional . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 852 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 859 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 860

Chapter Eighteen. Policy Development, Project Design, and Implementation . . . . . . . . . . . . . . . . . . . 871 Stacie Krick Evans and Sabrina W. Cole Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 872 Policy Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 872 Project Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 882 Project Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 890 Project Closeout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 891 Project Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 891 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 893 Case Study 18–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 893 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 894

Chapter Nineteen. Drug Information in Ambulator y Care . . . . . . 899 Debra L. Parker Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 900 Why Focus on Drug Information Specifically in the Ambulatory Care Setting? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 901 Providing Drug Information in the Ambulatory Setting . . . . . . . . . . . . . . . . . 902 Drug Information Responsibilities in Ambulatory Care . . . . . . . . . . . . . . . . . 904 Case Study 19–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 910 Patient Disposal of Unused Medications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 916 Case Study 19–2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 919 Providing Immunization Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 919 Quality-Assurance Considerations in Ambulatory Care . . . . . . . . . . . . . . . . . 921 Case Study 19–3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 922 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 923 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 923

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Chapter Twenty. Drug Information and Contemporar y Community Pharmacy Practice . . . . . . . . . . . . . 929 Morgan L. Sperry and Heather A. Pace Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 930 Pharmacists as Drug Information Providers in the Community Setting . . . . 932 Current Patient Sources of Drug Information . . . . . . . . . . . . . . . . . . . . . . . . . 933 Case Study 20–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 937 Case Study 20–2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 941 Case Study 20–3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 942 A New Model of Drug Information in the Community Pharmacy . . . . . . . . . 942 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 947 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 948

Chapter Twenty-One. Drug Information Education and Training . . 955 Michelle W. McCarthy Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 956 Foundation Skill Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 956 Specialized Skill Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 960 Pursuing Specialty Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 963 Case Study 21–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 964 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 964 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 965 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 968

Chapter Twenty-Two. Pharmaceutical Industr y and Regulator y Agency Drug Information . . . . . 971 Jean E. Cunningham and Lindsay E. Davison Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 973 Opportunities for Health Professionals Within Industry . . . . . . . . . . . . . . . . 974 Regulation of Health Professionals in Industry . . . . . . . . . . . . . . . . . . . . . . . . 978 Case Study 22–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 980 Pharmaceutical Research and Manufacturers of America and the Code on Interactions with Health Care Professionals . . . . . . . . . . . . . . . . 981 Case Study 22–2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 981

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Fulfillment of Medical Information Requests . . . . . . . . . . . . . . . . . . . . . . . . . . 982 Case Study 22–3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 984 Adverse Event Reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 984 Case Study 22–4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 985 Staying Connected with the Pharmaceutical Industry . . . . . . . . . . . . . . . . . . 985 Anatomy of Federal Agencies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 986 Case Study 22–5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 992 Case Study 22–6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 993 Division of Drug Information at the FDA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 993 Case Study 22–7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 999 Opportunities Within FDA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1000 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1002 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1002

Chapter Twenty-Three. Assessing Drug Promotions. . . . . . . . . . 1011 Robert D. Beckett and Genevieve Lynn Ness Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1012 WHO Ethical Criteria for Medicinal Drug Promotion . . . . . . . . . . . . . . . . . 1013 Direct-to-Consumer Advertising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1014 Case Study 23–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1024 Promotions to Health Care Professionals . . . . . . . . . . . . . . . . . . . . . . . . . . . 1025 Case Study 23–2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1030 Case Study 23–3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1031 Case Study 23–4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1034 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1035 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1036

Chapter Twenty-Four. Pharmacy Informatics: Enabling Safe and Efficacious Medication Use. . . . . . . . 1045 Joshua C. Hollingsworth and Brent I. Fox Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1046 Medication Use Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1047 Pharmacy Informatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1049 Order entry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1049

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Transcription . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1054 Dispensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1055 Administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1058 Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1058 Case Study 24–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1060 The Future: Informatics in the U.S. Health Care System . . . . . . . . . . . . . . . 1060 Case Study 24–2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1061 Interoperable Electronic Health Records . . . . . . . . . . . . . . . . . . . . . . . . . . . 1062 Case Study 24–3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1064 Greater Emphasis on Security, Privacy, and Confidentiality of Protected Health Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1064 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1066 Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1066 Appendix 2–1 Drug Consultation Request Form . . . . . . . . . . . . . . . . . . . . . . . . 1075 Appendix 2–2 Examples of Questions for Obtaining Background Information from Requestors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1077 Appendix 3–1 Performing a PubMed Search . . . . . . . . . . . . . . . . . . . . . . . . . . . 1082 Appendix 3–2 Selected Primary Literatures Sources . . . . . . . . . . . . . . . . . . . . . 1086 Appendix 4–1 Questions for Assessing Clinical Trials . . . . . . . . . . . . . . . . . . . . 1089 Appendix 5–1 Beyond the Basics: Questions to Consider for Critique of Primary Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1092 Appendix 7–1 Grade Evidence Profile: Antibiotics for Children with Acute Otitis Media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1098 Appendix 9–1 Question Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1100 Appendix 9–2 Abstracts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1102 Appendix 9–3 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1108 Appendix 11–1 Code of Ethics for Pharmacists . . . . . . . . . . . . . . . . . . . . . . . . . 1121 Appendix 12–1 Pharmacy and Therapeutics Committee Procedure . . . . . . . . . 1123 Appendix 12–2 Formulary Request Form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1137 Appendix 12–3 P&T Committee Meeting Attributes . . . . . . . . . . . . . . . . . . . . . 1139 Appendix 12–4 Example P&T Committee Minutes. . . . . . . . . . . . . . . . . . . . . . . 1141 Appendix 12–5 Chairperson Skills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1143

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Appendix 12–6 Conflict-of-Interest Declaration . . . . . . . . . . . . . . . . . . . . . . . . . 1145 Appendix 13–1 Format for Drug Monograph . . . . . . . . . . . . . . . . . . . . . . . . . . . 1147 Appendix 13–2 Example Drug Monograph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1150 Appendix 14–1 Tools Used in Quality Assurance . . . . . . . . . . . . . . . . . . . . . . . . 1156 Appendix 14–2 Example of Criteria and Request for Approval . . . . . . . . . . . . . 1161 Appendix 14–3 Example of MUE Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1162 Appendix 14–4 Evaluation Form for Drug Information Response . . . . . . . . . . . 1165 Appendix 15–1 Kramer Questionnaire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1166 Appendix 15–2 Naranjo Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1173 Appendix 15–3 Jones Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1174 Appendix 15–4 MedWatch Form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1175 Appendix 17–1 Investigational New Drug Application . . . . . . . . . . . . . . . . . . . . 1178 Appendix 17–2 Statement of Investigator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1180 Appendix 17–3 Protocol Medication Economic Analysis . . . . . . . . . . . . . . . . . . 1182 Appendix 17–4 Investigational Drug Accountability Record . . . . . . . . . . . . . . . 1183 Appendix 18–1 Policy Example: High-Alert Medications . . . . . . . . . . . . . . . . . . 1184 Appendix 18–2 Policy Example: Medication Shortages and Backorders . . . . . 1189 Appendix 22–1 Response Letter Drug A—Incidence of Yellow Stripes . . . . . . . 1194 Glossary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1197 Answers for Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1233 Answers for Self-Assessment Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1263 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1269

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Contributors Robert D. Beckett, PharmD, BCPS Assistant Professor of Pharmacy Practice Director, Drug Information Center College of Pharmacy Manchester University Fort Wayne, Indiana Chapter 23 Elaine Blythe, PharmD Associate Professor of Veterinary Medicine School of Veterinary Medicine St. Matthew’s University Omaha, Nebraska Chapter 3 Patrick J. Br yant, PharmD, FSCIP Director, Drug Information Center Clinical Professor, Pharmacy Practice and Administration School of Pharmacy University of Missouri—Kansas City Kansas City, Missouri Chapter 5 Karim Anton Calis, PharmD, MPH, FASHP, FCCP Adjunct Clinical Investigator Office of the Clinical Director

Eunice Kennedy Shriver National Institute of Child Health and Human Development National Institutes of Health Bethesda, Maryland Clinical Professor University of Maryland Baltimore, Maryland Clinical Professor Virginia Commonwealth University Richmond, Virginia Chapter 2 Zara Risoldi Cochrane, PharmD, MS, FASCP Assistant Professor of Pharmacy Practice Creighton University School of Pharmacy and Health Professions Omaha, Nebraska Chapter 15 Sabrina W. Cole, PharmD, BCPS Director of Biomedical Informatics Assistant Professor of Pharmacy School of Pharmacy Wingate University Wingate, North Carolina Chapter 18

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CONTRIBUTORS

Kathr yn A. Crea, PharmD, BCPS, CPPS Director of Accreditation and Patient Safety Officer OhioHealth—Riverside Methodist Hospital Columbus, Ohio Chapter 16 Jean E. Cunningham, PharmD, BCPS Clinical Content Specialist Truven Health Analytics Greenwood Village, Colorado Chapter 22 Lindsay E. Davison, PharmD Silver Spring, Maryland Chapter 22 Elizabeth A. Van Dyke, PharmD Drug Information Consultant Saint Clairsville, Ohio (independent consulting) Chapter 5 Stacie Krick Evans, PharmD Pharmacy Implementation Manager Central Atlantic Region Performance Services VHA, Waxhaw Chapter 18 Nancy L. Fagan, PharmD Assistant Professor of Pharmacy Practice Creighton University School of Pharmacy and Health Professions Omaha, Nebraska Chapters 12 and 13

Brent I. Fox, PharmD, PhD Associate Professor, Department of Health Outcomes Research & Policy Harrison School of Pharmacy Auburn University Auburn, Alabama Chapter 24 Maisha Kelly Freeman, PharmD, MS, BCPS, FASCP Associate Professor of Pharmacy Practice Director, Samford University Global Drug Information Service Birmingham, Alabama Chapter 4 Mar y Lea Gora-Harper, PharmD Clinical Pharmacist University of Kentucky Lexington, Kentucky Chapter 1 Philip J. Gregor y, PharmD, FACN Associate Professor of Pharmacy Practice Creighton University School of Pharmacy and Health Professions Omaha, Nebraska Chapter 15 Bambi Grilley, RPh, RAC, CCRA, CCRC, CIP Assistant Professor, Pediatrics Director, Clinical Research and Early Product Development Center for Cell and Gene Therapy Baylor College of Medicine Houston, Texas Chapter 17

CONTRIBUTORS

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Darren Hein, PharmD Fellow, Drug Information & Evidence-Based Practice Creighton University School of Pharmacy and Health Professions Omaha, Nebraska Chapter 15

Mark A. Malesker, PharmD, FCCP, FASHP, BCPS Professor of Pharmacy Practice and Medicine Creighton University School of Pharmacy and Health Professions Omaha, Nebraska Chapters 12 and 13

Joshua C. Hollingsworth, PharmD Doctoral Student, Department of Health Outcomes Research & Policy Harrison School of Pharmacy Auburn University Auburn, Alabama Chapter 24

J. Russell May, PharmD, FASHP Clinical Professor College of Pharmacy University of Georgia Augusta, Georgia Chapter 1

Peter J. Hughes, PharmD Assistant Professor McWhorter School of Pharmacy Samford University Birmingham, Alabama Chapter 4 Vicki R. Kee, PharmD Assistant Professor (Clinical) Drug Information Pharmacist The University of Iowa Iowa Drug Information Service Iowa City, Iowa Chapter 7 Michael G. Kendrach, PharmD, FASHP Professor and Associate Dean of Academic Affairs McWhorter School of Pharmacy Samford University Birmingham, Alabama Chapter 4

Michelle W. McCarthy, PharmD, FASHP Director, PGY1-Pharmacy and PGY2-Drug Information Residency Programs Director, Medication Use Policy and Compliance University of Virginia Health System Department of Pharmacy Services Charlottesville, Virginia Chapter 21 Cydney E. McQueen, PharmD Clinical Associate Professor, Pharmacy Practice and Administration University of Missouri–Kansas City School of Pharmacy Kansas City, Missouri Chapter 5 Kevin G. Moores, PharmD Associate Professor (Clinical) Director, Division of Drug Information Service College of Pharmacy The University of Iowa Iowa City, Iowa Chapter 7

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David P. Nau, PhD, RPh, CPHQ, FAPhA President Pharmacy Quality Solutions, Inc. Springfield, Virginia Chapter 14 Paul J. Nelson, MD Primary Physician Family Health Care, P.C. Adjunct Assistant Professor—Clinician of Pediatrics University of Nebraska Medical Center College of Medicine Omaha, Nebraska Chapters 12 and 13 Genevieve Lynn Ness, PharmD Director, Christy Houston Foundation Drug Information Center Assistant Professor in Pharmaceutical, Social and Administrative Science College of Pharmacy Belmont University Nashville, Tennessee Chapter 23 Linda K. Ohri, PharmD, MPH Associate Professor of Pharmacy Practice Creighton University School of Pharmacy and Health Professions Omaha, Nebraska Chapter 11 Heather A. Pace, PharmD Assistant Director, Drug Information Center Clinical Associate Professor University of Missouri–Kansas City School of Pharmacy Kansas City, Missouri Chapter 20

Debra L. Parker, PharmD Chair and Associate Professor Department of Pharmacy Practice The University of Findlay College of Pharmacy Findlay, Ohio Chapter 19 Karen L. Rascati, PhD Professor, Health Outcomes and Pharmacy Practice College of Pharmacy University of Texas Health Outcomes and Pharmacy Practice Division Austin, Texas Chapter 6 Martha M. Rumore, PharmD, JD LLM, FAPhA Assistant Director, Pharmacy Clinical & Educational Services Director, Pediatric Medication Resource Center Cohen Children’s Medical Center Professor, Pharmacy & Health Outcomes Touro College of Pharmacy Sorrell, Lenna & Schmidt, LLP New York, New York Chapter 10 Amy Heck Sheehan, PharmD Drug Information Specialist Indiana University Health Associate Professor of Pharmacy Practice Purdue University College of Pharmacy Indianapolis, Indiana Chapter 2

CONTRIBUTORS

Kelly M. Shields, PharmD Assistant Dean and Associate Professor of Pharmacy Practice Raabe College of Pharmacy Ohio Northern University Ada, Ohio Chapter 3 Morgan L. Sperr y, PharmD Clinical Assistant Professor Assistant Director, Drug Information Center University of Missouri–Kansas City School of Pharmacy Kansas City, Missouri Chapter 20

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Ryan W. Walters, MS Research Analyst and Instructor Division of Clinical Research and Evaluative Sciences Department of Medicine Creighton University Medical Center Omaha, Nebraska Chapter 8 James P. Wilson, PharmD, PhD Associate Professor of Health Outcomes and Pharmacy Practice College of Pharmacy University of Texas Health Outcomes and Pharmacy Practice Division Austin, Texas Chapter 6

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Preface Ever since the publication of the first edition of this book in 1996, there has been increasing realization of the importance of drug information. Much of this can be related to Internet information sources, along with the ever-increasing ease by which material can be located and used. This increased emphasis on information has had an effect on both the health care professional, who uses the material, and the patient, who may look up material directly and even bring it in to talk about with a health care professional. The ability to obtain, manage, evaluate, and use information has become an important core skill for the professional. This book was originally written to provide training in drug information management. It has been tested and refined continuously, based on experience in both practice and the classroom. In this fifth edition, the goal of this book continues to be to educate both students and practitioners on how to efficiently research, interpret, evaluate, collate, and disseminate information in the most usable form. While there is no one right method to perform these professional responsibilities, proven methods are presented and demonstrated. Also, seldom-addressed issues are covered, such as the legal and ethical considerations of providing information. The fifth edition continues to expand, updating information from previous editions and going into new areas. This includes a new chapter on assessing drug promotions by pharmaceutical representatives and the need for counterdetailing. Another new chapter is on pharmacy informatics. It always seemed that there should be a connection between pharmacy informatics and drug information—after all, both make extensive use of computer systems to manage information. However, they often seem to be treated as separate subjects. This new chapter bridges that gap.

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SPECIAL TEXT FEATURES In addition, we continue to include features that will assist the reader in learning the material: • A series of Key Concepts at the beginning of each chapter are then identified throughout the chapter using circled numbers placed within the text. • The main areas outlined in the Learning Objectives have been highlighted throughout the chapter using vertical rules placed in the margins alongside the relevant passages of text. • Most chapters have cases, where a situation that might be faced by a practitioner is described, with a series of discussion points presented. • A series of multiple-choice questions are included and many chapters provide suggested readings for further information on topics. As in previous editions, the book begins by introducing the concept of drug information, including its history, and providing information on various places in which drug information specialists may be employed. This is followed by information on how to answer a question, from the process of gathering necessary background information, through determining the actual information need, to answering the question. The chapter on drug information resources includes descriptions of the most commonly used references and contains information on apps available for practitioners. The drug literature evaluation chapters have been updated and expanded to cover newer concepts, such as adaptive clinical trials. Chapters from the previous edition have been updated. As always, numerous practical examples are provided through the chapters and in the appendices. With the veritable Niagara Falls of drug, medical, and pharmacy information available, much of which is complex, health care professionals have an increasing need for information management skills. This book will assist health care professionals and students with improvement in drug information skills and will allow individuals to evolve into new roles for the advancement of the profession and patient care. The authors and editors of this book hope the readers enjoy their journey toward expertise in information management.

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C hapter One

Introduction to the Concept of Drug Information Mary Lea Gora-Harper • J. Russell May

Learning Objectives After completing this chapter, the reader will be able to •

• • • •

•

•

Define the term drug information, used in different contexts, and relate it to the term medication information. Identify the services provided by drug information centers. Describe the skills needed to perform medication information functions. Identify major factors that have influenced the ability to provide medication information. Describe how the expanding integration of information technology has changed the methods of searching, analyzing, and providing medication information to patients and health care professionals. Describe how the increase in cost of health care has expanded the role of medication information specialists. Describe practice opportunities for a medication information specialist.

Key Concepts 1 Medication information may be patient-specific or developed for a given patient

population. 2 Medication information provision has evolved in the last 50 years as focus has shifted to medication safety, advances in informatics, evidence-based medicine, and new environments of care. 1

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

3 With computerized medical records and order entry systems, medication information

specialists can take a leadership role in incorporating automated interventions that improve safety and provide education at the point of prescribing. 4 Medication information specialists must keep abreast of advances in information technology. 5 Medication literature evaluation skills are essential. 6 Leadership and career opportunities exist in a variety of settings for medication information specialists.

Introduction The United States (U.S.) health care system is undergoing important changes, which is offering challenges and opportunities for health care providers, insurers, caregivers, and consumers. Several factors are driving these changes including new regulations in health care (e.g., expanded coverage for preventive care for patients of all ages, coverage regardless of preexisting conditions, the mandate for all Americans to acquire health insurance, expansion of Medicaid), the continued pressure to reduce health care costs, and the need to improve efficiency, quality, and safety of care.1-3 The appropriate use of pharmaceuticals continues to be an essential element in this process because drugs represent a significant portion of the health care dollars spent in the United States. Total health care system spending on medication reached $320 billion in 2011, an increase of about $50 billion since 2006 and $125 billion since 2002.2 The availability of patient-, disease-, and medication-specific information, and a knowledgeable decision maker are integral components of providing a system that supports the safe and appropriate use of medications. The provision of medication information is among the most fundamental responsibilities of pharmacists. 1 Medication information may be patient-specific, or developed for a given patient population, such as therapeutic guidelines, communication of a national quality initiative, coordination of an adverse drug event reporting and analysis program, publication of an electronic newsletter, or updating a Web site. The pharmacist can serve as a resource for issues regarding cost-effective medication selection and use, medication policy decisions (drug benefits), medication information resource selection, or practicerelated issues. Medication information opportunities are developing and expanding with changes in the health care environment, national efforts to expand access to care while reducing health care costs, the rise in the self-care movement, and the integration of new

CHAPTER 1. INTRODUCTION TO THE CONCEPT OF DRUG INFORMATION

3

health information technologies (e.g., electronic health record [EHR], telemedicine, social media, computerized physician order entry [CPOE], communication across settings of care). Medication information opportunities are growing in several areas within the health care environment including managed care organizations, the pharmaceutical industry, medical and specialty care clinics, scientific writing and medical communication companies, and the insurance industry. The term drug information may have different meanings to different people depending on the context in which it is used. If asked to define this term, one could describe it as information found in a reference or verbalized by an individual that pertains to medications. In many cases, individuals put this term in different contexts by associating it with other words which include: 1. Specialist/practitioner/pharmacist/provider 2. Center/service/practice 3. Functions/skills The first group of words implies a specific individual, the second group implies a place, and the third implies activities and abilities of individuals. The term drug information will be used in these different contexts to describe the beginnings and evolution of this area of practice. Relative to current practice, the term medication information is used in place of drug information to convey the management and use of information on medication therapy and to signify the broader role that all pharmacists take in information provision. These terms may refer to either the provision of information for a specific patient or in the context of addressing medication use issues for a population (i.e., a group of individuals defined by a set of common characteristics, such as in a set of policies and procedures on medication use developed by health professional working in the emergency department of a hospital). Drug informatics refers to the electronic management of drug information. It emphasizes the use of technology as an integral tool in effectively organizing, analyzing, managing, and communicating information on medication use in patients. With the growing integration of EHRs and CPOE, there is greater opportunity to provide information on medications for individual patients at the point of care, and be able to assess outcomes more readily on a population of patients.4 The impact of new technologies and opportunities in drug informatics in current and future practice is discussed later in this chapter and Chapter 24. The goals of this chapter are to describe how the role of the pharmacist has evolved in providing medication information, to discuss factors contributing to that evolution, and to describe opportunities for use of medication information skills, either as a generalist or in a specialty practice. This chapter provides the foundation for understanding the pharmacist’s need to have proficiency in the knowledge and skills discussed in this book.

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

The Beginning The term drug information developed in the early 1960s when used in conjunction with the words center and specialist. In 1962, the first drug information center was opened at the University of Kentucky Medical Center.5 This center desired to be a source of comprehensive drug information for staff physicians, dentists, and nurses. Another goal was to take an active role in the education of health professional students including medicine, dentistry, and nursing and specifically influence pharmacy students in developing their role as drug consultants. Several other drug information centers were established shortly thereafter. The first formal survey, conducted in 1983, identified 54 pharmacist-operated centers in the United States.6 The individual responsible for operation of the center was called the drug information specialist. The expectation was that drug information would be stored in the center and retrieved, selected, evaluated, and disseminated by the specialist. As practice progressed, some drug information centers evolved to drug information services where drug information activities were provided outside of a formalized center. In addition, other specific functions evolved over time, as listed in Table 1–1. A drug information center or specialist may be involved in one or all of these functions. Detailed information regarding these activities is provided in subsequent chapters. In the 1960s, the availability of new drugs (e.g., neuromuscular blockers, firstgeneration cephalosporins) provided challenges for practitioners to keep abreast and make appropriate decisions for their patients. Part of the problem was finding a way to

TABLE 11. MEDICATION INFORMATION SERVICES • Supporting clinical services with medication • • • •

•

•

information Answering questions regarding medications Coordinating pharmacy and therapeutics committee activity Developing medication use policies Publishing or editing information on appropriate medication use through newsletters, journal columns, Web sites, e-mail, social media, etc. Managing investigational medication use (e.g., Institutional Review Board activities, information for practitioners—see Chapter 17) Providing poison information

• Coordinating formulary management initiatives • Developing criteria/guidelines for medication use • Analyzing the clinical and economic impact of drug

policy decisions • Managing medication usage evaluation/

medication use evaluation and other quality assurance/improvement activities • Providing education (e.g., in-services, classes, experiential education) for health professionals, students, and consumers • Coordinating of adverse drug event reporting and analysis programs, e.g., adverse medication reactions • Consulting on the development of informatics in the health system setting

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5

effectively communicate the wealth of information to those needing it. The information environment relied heavily on the print medium for storage, retrieval, and dissemination of information. MEDLARS® (Medical Literature Retrieval and Analysis System) was developed by the National Library of Medicine in the early 1960s.7 While it provided a computerized form of searching, requests for searches were submitted by mail and results returned by mail. The ability to transmit such information over telephone lines (online technology) was not available until 1971 when MEDLINE® was introduced and was limited to libraries. During this time, the drug information specialist was viewed as a person who could bridge the gap and effectively communicate drug information.8 Of course, methods for accessing information have evolved tremendously with the creation of the Internet and the integration of EHR. Early reports that examine the requirements for training of a drug information specialist recommended the following courses be added or strengthened in the pharmacy school curricula: biochemistry, anatomy, physiology, pathology, and biostatistics and experimental design (with some histology, embryology, and endocrinology incorporated into other courses).9 Such topics were either not incorporated or not emphasized in curricula of the 1960s. In today’s pharmacy curricula, most of these topics receive considerable emphasis. In addition to these subjects, progress has been made to incorporate evidence-based practice and patient-centered care into curricula.10 Pharmacists today use knowledge and skills to make clinical decisions about medication use in specific patients or a group of patients in conjunction with other health care professionals. This fits nicely with providing patient- and family-centered care, known as patient-centered medical home (PCMH). Training and expertise in evidence-based practice have led pharmacists to take a leadership role in publishing in the area of therapeutic guidelines, drug policy initiatives, or outcome analyses, sometimes with support of the pharmacy professional organizations. These activities illustrate how pharmacists play a major role in meeting the needs of individual patients and large patient groups.

The Evolution It is useful to look at the evolution of drug information practice from the perspective of drug information centers and practicing pharmacists. Calculating accurate numbers of drug information centers nationally or internationally (e.g., Puerto Rico, Japan, Saudi Arabia, Africa) is difficult, because no agency or organization is responsible for maintaining a list. Well-defined criteria are not established for using the titles of drug information centers/services. Some centers specialize in a particular area of drug information, and

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

their name may reflect that specific function (e.g., center of drug policy and drug information, drug and poison information center, and drug information and wellness center). Some centers limit their practice to a subset of clients (e.g., pharmacists, physicians, nurses, other health professionals, attorneys, faculty, consumers) based on their source of funding (e.g., pharmaceutical manufacturer, government, college of pharmacy, managed care organization, law firm, law enforcement agencies), and some drug information centers are available for all consumers, 7 days a week, 24 hours a day, as is the case for a drug and poison information center. The center may provide services via telephone, through a Web site, face-to-face with their clients, or other methods. One study conducted in 2008 and published in 2009 examined 89 drug information centers to test if there were changes in number or type of questions, and time spent on activities compared to 5 years earlier.11 Eighty-four percent of the drug information centers were still in existence. There was an increase in time spent educating students (53%) and supporting adverse drug reaction reporting initiatives (44%). Seventy-six reported an increase in the number of complex questions, with 53% documenting an increase in the time required to answer questions. When examining the availability of a drug information center specifically in the hospital setting, a 2010 survey that examined over 1950 U.S. hospitals, found that 5% had a formal drug information center as their source to provide objective drug information.12 In an earlier survey,13 it was found that the availability of a formal drug information center was more prevalent in larger hospitals. For instance, when examining a subset of the hospitals with more than 400 beds, 28.2% of hospitals reported that they had a formal drug information center. A few studies have described the economic benefit of maintaining a drug information center or related activity in an academic institution or hospital. One such study examined the economic impact of drug information services responding to patient-specific requests.14 The resultant cost-benefit ratio was found to be 2.9:1 to 13.2:1. Most of the cost savings resulted from a decreased need for monitoring (e.g., laboratory tests) or a decreased need for additional treatment related to an adverse effect. Another study examined the drug cost avoidance and revenue associated with the provision of investigational drug services, which was not part of drug information center in this study, but may be the responsibility of a drug information center.15 The annualized drug cost avoidance plus revenue was $2.6 million. Studies of this nature are becoming increasingly important in an era of cost containment. Although these studies were completed several years ago, the basic premise of the design and results are applicable today and can be used to provide a foundation for assessment of the value of a particular center based on location, clientele, and funding. Other literature also exists that evaluates the economic return on investment for clinical services, which can help provide a framework for how to assess the value of a drug information center.16

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DRUG INFORMATIONFROM CENTERS TO PRACTITIONERS The responsibilities of individual pharmacists regarding the provision of medication information have changed substantially over the years. The impetus for this change was provided not only by the development of drug information centers and the clinical pharmacy concept, but also by the Study Commission on Pharmacy.17 This external group was established to review the state of the practice and education of pharmacists and report its findings. One of the findings and recommendations stated that “… among deficiencies in the health care system, one is the unavailability of adequate information for those who consume, prescribe, dispense and administer drugs. This deficiency has resulted in inappropriate drug use and an unacceptable frequency of druginduced disease. Pharmacists are seen as health professionals who could make an important contribution to the health care system of the future by providing information about drugs to consumers and health professionals. Education and training of pharmacists now and in the future must be developed to meet these important responsibilities.”

The report of the Commission was issued in 1975, and since that time drug information practice has changed for both drug information centers and individual pharmacists. The development of clinical pharmacy has helped move pharmacy forward in recognizing its capabilities to contribute to the care of patients. Clinical pharmacy was thought of primarily as an institutional patient care process and did not gain widespread acceptance outside of hospitals. Over time, the activity of the pharmacist as a medication expert for patients has gained acceptance in a variety of practice settings including community pharmacies, nursing homes, and primary and specialty clinics. Pharmacists who provide patient-specific information with a goal of improving patient outcomes use the medical literature to support their choices.18 Pharmacists involved in patient care areas (e.g., hospitals, clinics, long-term care, home health care) now frequently answer medication information questions, participate in evaluating a patient’s drug therapy, and conduct medication usage evaluation activities. In one survey of more than 1960 hospitals, approximately 97% have staff pharmacists routinely answer drug information questions.12 The provision of medication information may be on a one-on-one basis or may occur using a more structured approach, such as a presentation to a class of patients with diabetes or a group of nurses in the practice facility. In either case, the pharmacist educates those who are the beneficiaries of the medication information. Pharmacists may also participate in precepting students in patient care or pharmacy environments. In any of these roles, the pharmacist must use appropriate information retrieval and evaluation skills to make sure that the most current and accurate information is provided to make decisions about medication use for those they are serving. This role of the pharmacist as a

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TABLE 12. MEDICATION INFORMATION SKILLS 1. 2. 3. 4. 5. 6. 7.

Assess available information and gather situational data needed to characterize question or issue. Formulate appropriate question(s). Use a systematic approach to find needed information. Evaluate information critically for validity and applicability. Develop, organize, and summarize response for question or issue. Communicate clearly when speaking or writing, at an appropriate level of understanding. Anticipate other information needs.

provider of medication information continues to be an important component of the educational outcomes developed by the Center for the Advancement of Pharmaceutical Education (CAPE). These outcomes are initiated and maintained by the American Association of Colleges of Pharmacy (AACP) to help transform the pharmacy curriculum to support education of the future.10,19 There is a well-described systematic approach to answering drug information questions (see Chapter 2). It is important to obtain the necessary background information including pertinent patient factors, disease factors, and medication-related factors to determine the true question. Good problem-solving skills are required to fully assess the situation, develop a search strategy (see Chapter 3), evaluate the information (see Chapters 4 and 5), and then formulate and communicate a response. Good communication skills are essential to respond in a clear and concise manner, using terminology that is consistent with the patients’, caregivers’, or health professionals’ level of understanding. Table 1–2 lists the medication information skills a pharmacist should possess when confronted with a medication information need. Opportunities continue to grow for pharmacist participation in the continuum of care including home health care and long-term care that require a solid therapeutic knowledge base, an understanding of the medical literature, and the ability to communicate the information through either verbal or written consultation. Pharmacists in community settings counsel patients, answer medication information questions, review patient medication regimens for potential problems (medication therapy management), and participate in helping patients manage chronic diseases. The PCMH philosophy has become a widely accepted model for how primary care should be organized and delivered throughout the health care system. Patient care is considered to be comprehensive, team-based, coordinated, and accessible. As a component, the health care team helps improve the quality of care through access to information technology and other tools, to help make sure that both patients and families are making informed choices about their health. Medication information should be administered when they need it, and in a culturally and linguistically appropriate manner.

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Opportunities for pharmacists are also available in the area of veterinary pharmacy practice. Information is needed by both the animal owner and the veterinarian. A pharmacist may need to practically apply information from veterinary resources (e.g., Veterinary Drug Handbook, Textbook of Veterinary Internal Medicine, National Animal Poison Control Center) for the benefit of an animal (see Chapter 3).

FACTORS INFLUENCING THE EVOLUTION OF THE PHARMACIST’S ROLE AS A MEDICATION INFORMATION PROVIDER 2 Medication information provision has evolved in the last 50 years as focus has shifted to medication safety, advances in informatics, evidence-based medicine, and new environments of care. Other factors include new regulations in health care, the changing philosophy of practice (e.g., patient-focused medical home programs), the evaluation of outcomes, the sophistication of medication therapy, and the self-care movement.

Adverse Drug Events As mentioned earlier in this chapter, one of the primary roles for drug information specialists in the beginning was collecting and evaluating adverse drug reactions.5 This role will continue to expand because it is anticipated that the number of adverse drug events (ADEs) will increase in the near future for several reasons: (1) the availability of new medications and new indications with conventional medications, (2) the growing elderly population, (3) the increased use of medications for disease prevention, and (4) the improved insurance coverage for medications.20 Pharmacists perform this function in institutional health systems, managed care, or the pharmaceutical industry. To illustrate how a central area for reporting ADEs, such as a drug information center in an institutional health system, can be beneficial, consider the following unpublished example from an academic medical center. The drug information center received three reports of patients developing methemoglobinemia within a 2-week period. The offending agent was suspected to be benzocaine spray. Upon investigation, the drug information pharmacist recognized that all reports had one thing in common: the administering nurse. The pharmacist witnessed the administration of the drug by the nurse the next time it was ordered for a patient. Instead of a single brief spray as directed by the prescribing information, several sprays were used resulting in a potentially toxic dose of drug. The drug information pharmacist developed a series of in-services for nurses. No reports of benzocaineinduced methemoglobinemia have occurred since. In managed care settings, the same benefit could be achieved on an even larger scale. The role of the drug information specialist in the pharmaceutical industry as it relates to reporting ADEs is especially important in postmarketing surveillance activities. Because of the specific definition of a study population using inclusion and exclusion

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criteria in a new drug trial, many ADEs go undetected until the agent is commercially available and used in a broader population. By quickly identifying potential problems and communicating them to health care professionals, patient safety may be improved. The training and expertise of the drug information specialist qualifies them to play a major role in this process. The importance of maintaining a comprehensive, multidisciplinary, ongoing program for monitoring, reporting, and resolving drug-related problems, and developing mechanisms to prevent future ADEs will continue to be an important element of managing medication use, with the drug information specialist providing leadership, as well as every clinical practitioner (e.g., pharmacists, nurses, physicians) contributing to the overall program. There is an estimated 700,000 emergency room visits and 120,000 hospitalizations annually attributed to ADEs, with an annual extra cost of $3.5 billion to the health care system.21,22 Forty percent of these events are considered to be preventable.22 These numbers are probably even higher in the elderly population because of age-related physiological changes, co-existing conditions, and polypharmacy.23 The prevention of ADEs is important today, and will continue to be a significant health care issue in the future.24

Integration of New Health Information Technologies Computer technology has drastically changed the ability to store and access information. The focus in medication information is driven toward the use and integration of data, information, knowledge, and technology involved in the medication use process to improve outcomes for patients. Even though the amount of literature is much larger today than in the past, it is more manageable. The Internet allows the user to easily access the scientific literature, government publications, news reports, and many other items, frequently without cost to the clinician or the consumer, and handheld devices (e.g., smartphones, tablets) have allowed practitioners to have a full range of applications (e.g., decision-support tools, medical references) that can be available at the point of care.25 These devices offer the convenience of collecting and accessing information from a unit that can be carried in a user’s pocket. In certain situations, these systems can be used more conveniently than a desktop computer for online searching, calculations, patient tracking, laboratory order entry, and results, to provide medication profiles, to set appointments, as a time-management tool, and to search drug information databases (e.g., general drug information text, medical specialty reference books, drug interaction resources). Patients and health care practitioners can find information on nearly every disease and treatment, and virtual health communities and forums provide a mutually supportive environment for patients and their families, and friends. The use of social media (e.g., Twitter®, Facebook®, LinkedIn), e-mail, Web forums, and blogs has simplified the way in which peers can exchange news and share opinions. Several professional organizations (e.g., American Society of Health-System Pharmacists [ASHP]; http://www.ashp.org)

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have used technology to maintain awareness of important news affecting pharmacy and the health care environment (e.g., regulatory and health policy issues), drug shortages, and awareness of their meetings. Live continuing education is offered at a clinician’s computer desktop through Webinars. There is an increasing need by health professionals, as well as consumers, to get more information about medications sooner. Information is needed quickly when a new medication becomes commercially available because of the potential for health and cost implications, when a product is withdrawn from the market for safety reasons, or when data from a new study is released that could have an impact on how common ailments are treated. The lag time that occurs with the print format may not be acceptable for many direct patient care issues. The Internet allows medical information to be available sooner to both health care professionals and the public. The availability of electronic journals and texts has minimized the need to travel to a library. Online repositories for articles, such as BioMed Central (http://www.biomedcentral.com) and PubMed® (http://www. pubmedcentral.nih.gov), have allowed individuals to access millions of articles quickly, easily, and free of charge. The majority of printed medical textbooks with an online version require a subscription; however, there are exceptions (e.g., http://www.merck.com, where eight editions of Merck Manuals can be viewed and searched for free). Registries of ongoing clinical trials, such as ClinicalTrials.gov (http://www.ClinicalTrials.gov), provide information on the purpose and criteria for participation in an ongoing clinical trial. This has allowed pharmacists to anticipate new therapies, and perhaps help their patients receive medications not yet approved by the Food and Drug Administration (FDA) through enrollment in a clinical trial. In addition to health professionals, patients are also accessing information from the Web, using sites that are sponsored by a variety of companies and individuals with diverse interests. In a recent survey, 85% of physician respondents had experienced a patient bringing Internet information to a visit.26 Information that is either incomplete or inaccurate may result in harmful behavior, such as discontinuing medication or increasing the doses.27 In one study,28 information on medications on Wikipedia was found to be more narrow in scope, and had more errors of omission than the comparator database on the Web (i.e., Medscape Drug Reference, which is a free, online, evidence-based, peerreviewed database). There is some effort toward helping consumers accurately assess the quality of information on the Internet. Health on the Net (http://www.hon.ch) is a nonprofit, nongovernment organization that uses criteria to assess the quality of a Web site. The organization will give a seal of approval to those sites that apply and meet the quality criteria. If misinformation or inaccurate information is found on the Web, organizations exist to monitor fraud (e.g., Quackwatch®; http://www.quackwatch.com). One site that may be helpful in providing patients with information on a range of medical conditions and

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management is healthfinder (http://www.healthfinder.gov). See Chapters 19 and 20 for more information. Drug information centers have created their own Web sites to post information about their centers and services, to provide links to related sites considered to be of acceptable quality, to accept adverse drug reaction reports, to receive and answer medication information questions conveniently, and to provide information regarding formulary changes, institution-specific therapeutic guidelines, and drug policy initiatives.29 The advantage of having a request form for answering medication information questions or reporting adverse drug reactions on the Web is that physicians, pharmacists, or other health professionals can access computers at their practice site. This information is typically accessible only through an institution’s intranet.30 An intranet is a network that belongs to an organization and is designed to be accessible only by the organization’s members, employees, or others with authorization. The Web site looks and acts just like other Web sites, but has a firewall surrounding it; therefore the center can provide easy access to their primary patrons, without receiving extraneous questions from outside their defined clientele.31-33 There is a massive effort nationally to modernize health care by making all medical records standardized and electronic.34 This is considered to be the cornerstone for improvements in quality of care, patient safety, and efficiencies, all leading to an economic benefit.35,36 A properly configured medical record provides decision support, facilitates workflow, and enables the routine collection of data for performance feedback in an effort to help improve efficiency and quality of care, including patient safety.37-40 3 With computerized medical records and order entry systems, medication information specialists can take a leadership role in incorporating automated interventions that improve safety and provide education at the point of prescribing. The use of computer-based clinical decision support systems (CDSS) (see Chapter 24) that provide patient information with recommendations based on the best evidence is shown to be valuable in the patient care setting, including a reported decrease in length of hospital stay.41,42 In one study that examined the value of using a decision support program to assist physicians in using anti-infective agents, the length of hospital stay of patients who used the recommendations was compared with a group of patients who did not always use the recommendations, and compared against a group of patients who were admitted to the unit 2 years before the intervention program.41 The length of hospital stay was statistically different with an average of 10 days, 16.7 days, and 12.9 days. Although technology affords remote-site access to medication information sources, it is critical that pharmacists have the skills to perceive, assess, and evaluate the information, and apply the information to the situation. One of the most rapidly changing technologies in health care is information technology. 4 Medication information specialists must keep abreast of advances in information technology in an effort to integrate new and

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valuable systems in a timely and efficient manner. The need for this type of training is emphasized in an Institute of Medicine (IOM) report.43

FOCUS ON EVIDENCEBASED MEDICINE AND DRUG POLICY DEVELOPMENT The pharmacist’s ability to apply their medication information skills to drug policy decisions will be of growing importance in this changing health care environment. This can be done by identifying trends of inappropriate medication use in a group of patients and providing supporting scientific evidence to help change behavior. Continued growth in national health expenditures has raised the concern of government, insurance agencies, health care providers, and the public in identifying strategies to control spending while maintaining access to quality health care. The United States spent more than $307.5 billion on prescription drugs in 2010.2 In 2014, national health spending is projected to raise to 7.4% (approximately 2 percentage points faster than without reform), with the policy changes from the Affordable Care Act (ACA) expecting to result in 22 million fewer uninsured people. There is also an anticipated increase in Medicaid spending of 18% and private health insurance growth of approximately 8%.44 Because drug expenditures are the largest component of the pharmacy operating budget, and a significant portion of the entire health system budget, the pharmacy budget frequently attracts significant attention from leadership. In recent years, there has been a shift from a fee-for-service, inpatient focus, to a capitated, managed care, ambulatory focus.45 Managed care, a process seeking to manage the delivery of high-quality health care in order to improve cost-effectiveness, is consuming an ever-increasing portion of health care delivery. Today, providers are relying less on impressions of what may be happening in a practice setting, and more on data that are actually being collected in that same group of patients (e.g., number of patients receiving appropriate dose of drugs). Goals are set for a particular group of patients (e.g., all patients receive beta-blocker therapy after a myocardial infarction) based on evidence found in the scientific literature. This connection of applying the scientific information to the patient care setting is made through evidence-based medicine. Evidence-based medicine (see Chapter 7) is an approach to practice and teaching that integrates current clinical research evidence with pathophysiological rationale, professional expertise, and patient preferences to make decisions for a population.46 5 Medication literature evaluation skills are essential. Pharmacists need to have a solid understanding of medication information concepts and skills, be able to evaluate the medication use issues for a group of patients, be able to search, retrieve, and critically evaluate the scientific literature, and apply the information to the targeted group of patients. Evidence-based medicine techniques are used in health care organizations in the development and implementation of a variety of quality assurance tools (e.g., therapeutic guidelines, clinical pathways, medication use evaluations, and disease state management)

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in an effort to improve patient outcomes and decrease costs across the health care system. The goal is to support the appropriate use of medications including correcting the overuse, underuse, or misuse of medicines. In the United States, the IOM designated evidence-based patient-centered health care delivery as a key feature of high-quality medical care.47 All of these situations require pharmacists to use drug information skills and to have various kinds of medication information support at the practice site or easily accessible at a remote site. The process of evidence-based medicine requires that systems be developed to measure and report processes and outcomes that can be used to drive quality improvement efforts. Data can be collected and analyzed by a medication information specialist using scientific methods to support the decision-making process in a managed care organization.48 Outcomes research is a type of investigation that uses scientific rigor to determine which interventions are best for certain types of patients and under certain circumstances. This contrasts with traditional randomized controlled studies to determine efficacy, which examines the success of treatments in controlled environments. Outcomes research, taking place in real-life settings, is called effectiveness research. The branch of outcomes research, pharmacoeconomics, provides tools to assess cost, consequences (e.g., quality of life, patient functionality, patient preferences), and efficiency (see Chapter 6).49,50 These types of publications can help guide the practitioner in developing guidelines on appropriate medication use in their practice settings. This will be discussed more fully in Chapter 7.

Sophistication of Medication Therapy The sophisticated level of medication therapy that occurs today provides pharmacists much more opportunity to lend their expertise in assessing medication information needs of professionals, patients, or family members, and providing literature to help choose the best medication to use within a class, to convey the appropriate information to help patients correctly and safely use the more potent medications, and to address administration and delivery problems. It is increasingly difficult for health professionals to keep up with all of the developments in medication therapy, as well as keep abreast of the other information required for their practice. It is estimated that over 5400 compounds are in various stages of clinical drug development.51 Nearly 78% of the projects in the pipeline study medications that attack a disease in a way that is unique to any other existing medicine. Several of the drugs in the different stages of development could have a substantial impact on clinical practice and drug expenditures once they are commercially available. For instance, it is anticipated that at least 3400 of these medications are anticancer agents, which could have an impact on life expectancy, quality of life, and the related expenses associated with the potential need for increased ancillary care, additional physician office visits, or hospitalization.51 It is important that drugs in the pipeline be monitored by

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pharmacists to provide adequate time to identify the patient population that will most benefit from the new drug and to help anticipate the cost of treating these patients compared to traditional therapy.2 See Chapter 17 for more information. There is also a trend toward individualization of health care using pharmacogenomic profiling to determine potential drug effectiveness.52 Patients may be tested for genomic patterns, and their drug therapy will be altered accordingly. There are several potential benefits of using this pharmacogenomic technique: new effective treatments for a variety of medical conditions could be identified faster and in smaller samples, computer modeling can help eliminate the medications that do not work, and, because this technique can help identify the best candidates for a particular drug, it can help patients sooner.53 Over 155 trials are studying personalized medicine, which uses an individual’s genetic profile to guide decisions on the diagnosis and treatment of disease.51 There are medications that use cell therapy, antisense RNA interference therapy, monoclonal antibodies joined to cytotoxic agents to help target tumor cells, and gene therapy. As the types and sophistication of medication therapy continue to evolve, this will provide challenges in the future for patients, family members, and practitioners who want information on viable candidates for medication therapy, to address administration and delivery issues, and assess outcomes in real-life settings. The ability to assess information needs; search, analyze, and retrieve appropriate literature; and apply the information to patients will be important.

Rise in the Self-Care Movement Finally, consumers have a continually growing desire for information about their medications (see Chapter 20). The growth of the self-care movement, the increase in focus on health care costs, and the improved accessibility of health information are some of the factors that have influenced patients to participate more fully in health care decisions, including the selection and use of medications. Based on these needs, direct-to-consumer advertising (DTCA) campaigns have appeared in virtually all mediums including magazines, television ads, Web-based ads (e.g., through e-mail, search engine marketing, or banner style ads on specific Web sites), and radio reports (see Chapter 23). There may be some benefits to DTCA for the patient and overall health care system.54,55 The ads could serve to inform patients on the management of a particular disease or condition, or the appropriate use of a medication being marketed. These advertisements can also be viewed as empowering patients to have a more active role in their own health care, and for patients already taking a certain drug, the advertisement could serve as a reminder to take the medication, ultimately improving patient compliance.54 There are also clearly negative aspects of DTCA. There is potential for this information to result in the increased use of the advertised drugs when less expensive alternatives may be more appropriate, resulting in increases in drug spending and utilization. Patients may also lack the skills needed to evaluate comprehensive medical information,

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even if it has been provided.55,56 This is because the content in DTCA often exceeds the eighth-grade reading level, which is typically recommended for information distributed to the general public. Paradoxically, the inclusion of information about risks and adverse events in DTCA may also promote an unnecessary fear of side effects, which may result in nonadherence. In general, information provided with at least some prescription drugs is not adequately understood by less-informed consumers and does not effectively communicate critical safety messages or directions.56 Health information is one of the most frequently searched topics on the Internet. In 2012, a study conducted by the Pew Research Center examined the use of the Internet for health information online.57 Interestingly, 35% of U.S. adults have used the Internet to research a medical condition, and of these, half have followed up with a medical professional. Eighty-five percent of U.S. adults own a cell phone, and of those 31% say they have used their phones to look for health information online. Because a single individual is able to serve as an author, editor, and publisher of a Web site, there is no safeguard on the quality of information available on the Internet (see Chapter 3). The end result may be a highly informed or perhaps misinformed consumer.58-60 When a patient finds information about medications that they are either considering to start taking or are currently taking, from the Internet, through the lay press, or by DTCA, a pharmacist can help consumers critically assess the medication information that they find and add to the information based on specific patient-related needs. See Chapter 23 for more information. The need to critically assess information regarding complementary and alternative medicine (CAM) has become increasingly important, with approximately 38% of U.S. adults aged 18 years and over and approximately 12% of children use some form of CAM.61 The use of CAM (e.g., herbal or dietary supplements, meditation, chiropractic care, and acupuncture) is widespread. The 2007 National Health Interview Survey (NHIS), a nationwide government survey, found that 38% of U.S. adults reported using CAM in the previous 12 months, with the highest rates among people aged 50 to 59 (44%).62 The NHIS data also revealed that approximately 42% of adults who used CAM in the past 12 months disclosed their use of CAM to a physician (MD) or osteopathic physician (DO).63 Because many adults also use nonprescription medications, prescription drugs, or other conventional medical approaches to manage their health, communication between patients and health care providers about CAM and conventional therapies is vital to ensuring safe, integrated use of all health care approaches. There is a trend toward integrating CAM with conventional medicine. In a survey of over 6400 U.S. hospitals, approximately 37% offered some sort of CAM options. This is increased from 26.5% in 2005.64,65 Eighty-four percent and 67% of respondents claimed that patient demand and clinical effectiveness, respectively, were the primary rationale for offering CAM services. This area presents a challenging situation for pharmacists

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because of the need to assess relevant outcomes from well-designed clinical trials. Consumers are increasingly interested in finding reliable information regarding these products. Pharmacists are in an excellent position to help provide such information. One drug information center describes its experience with a devoted telephone line to provide information regarding herbal supplements.66 There was an increased demand for the service over time based on a higher call volume. This is consistent with the growing use of CAM nationally. They also described the challenges and limitations of finding reliable information on herbal products. Several resources are available that have information on herbal products.67 It is just as important that the pharmacist provide information from reliable sources, as well as identify information that is lacking in regard to a particular product (see Chapter 5). Groups like the National Council on Patient Information and Education (NCPIE) encourage patients to seek information when they have questions. The experiences with some public access medication information hotlines have indicated the public desire and need for information.68 Such hotlines, often established by pharmacists, are intended to enhance the relationships between health care professionals and patients. The changing environment affords the pharmacist many opportunities to use the full spectrum of medication information skills. Factors such as the integration of new technologies, the focus on evidence-based medicine and drug policy development, the sophistication of medication therapy, and the rise in self-care movement require that all pharmacists have a strong foundation in medication information concepts.

EDUCATING STUDENTS ON MEDICATION INFORMATION CONCEPTS The education of pharmacists continues to evolve in scope and depth. Many of the areas identified earlier as needed by the drug (medication) information specialist are now incorporated into pharmacy curricula and taught to all student pharmacists. In 1991, a consensus conference in New Mexico was held to define a set of objectives for didactic and experiential training in drug information for the year 2000.69 Twenty-three educators and practitioners participated in the conference. There were several key concepts that were developed including: (1) drug information should be a required component of the pharmacy curriculum and include both didactic and competency-based experiential components, (2) drug information concepts and skills should be spread throughout the curriculum, beginning the day the students enter pharmacy school, and (3) problem solving should be a major technique in drug information education, with the goal of developing self-directed learners. Developing these skills should provide the foundation for the pharmacist to be a life-long learner and problem solver. Based upon the work of this conference, as well as changes in the health care system, and the movement toward outcome-based education, colleges of pharmacy are redesigning their curricula to

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

provide a more comprehensive and integrated approach to teaching medication information concepts and skills.70,71 The CAPE outcomes, which are guidelines used for pharmacy education, continue to include a medication information skills for all student pharmacists.19 In a recent survey, all pharmacy schools offered didactic drug information education to first professional year students as either a stand-alone course (70%) or an integrated course throughout the professional curriculum.72 Fifty-one of the 60 colleges offered an advanced pharmacy practice experience (APPE) in drug information, and 62% of these had it as an elective. However, 58% of respondents felt they had an inadequate number of drug information training sites. Communication skills are taught formally to facilitate the pharmacist’s ability to transmit information to both health professionals and patients. In 2009, the American College of Clinical Pharmacy (ACCP) Drug Information Practice and Research Network (DI PRN) published an opinion paper that provides recommendations regarding the curriculum and instructional methods for teaching drug information in both colleges of pharmacy and advanced training to help meet the needs of the changing health care environment and the changing culture of drug information practice.73 In a follow-up survey examining which recommendations were included in U.S. pharmacy college curricula in the areas of drug information, literature evaluation, and biostatistics, less than half of the core concepts outlined in the opinion paper (i.e., 9 [47%]) were included in curricula of all responding institutions.74 This supports the need to continually reevaluate and update the curriculum that focuses on medication information concepts because of changes in the health care environment. Of note, many respondents identified the areas of evidence-based medicine, medication safety, and informatics as areas of expanded focus. Technology (e.g., Twitter in a pharmacy management course, and use of e-portfolios) is also being integrated in the education process within the colleges of pharmacy.75,76 The evolution in technology and social media has changed the way faculty teach, students learn, and faculty and students communicate in colleges of pharmacy. An academic technologist can support the college faculty and staff in the use of learning management systems, distance education programs, and classroom technology. Upon graduation, a pharmacist can choose to enter the workforce or continue their education in a practice-based mentorship in a residency or fellowship. Postgraduate training through residencies and fellowship experiences can help prepare a pharmacist to be a skilled clinical practitioner, researcher, educator, and leader in the profession of pharmacy. Medication information and policy development are integrated throughout the three goal areas addressed in the pharmacy practice residency standards. Currently, there are 14 ASHP-accredited specialty practice (PGY-2) residencies in medication information with a total of 19 available positions (http://www.ashp.org/menu/Accreditation/ ResidencyDirectory.aspx). These were designed for those who practice in health

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systems. Individuals who specialize in drug information can practice in a variety of different areas (e.g., scientific writing and medical communications). See Chapter 21 for more information.

Opportunities in Specialty Practice As the role of the practicing pharmacist has changed regarding medication information activities, so has the role of the specialist. The role of the medication information specialist has changed from an individual who specifically answers questions to one who focuses on development of medication policies and provides information on complex medication information questions. 6 Leadership and career opportunities exist in a variety of settings for medication information specialists, including a contract drug information center, medical informatics, managed care organizations (e.g., health maintenance organizations [HMOs]/pharmacy benefit management organizations [PBMs]), scientific writing and medical communications, poison control, pharmaceutical industry, and academia. A specialist in medication information can be involved in multiple activities in practice settings listed in the following.

CONTRACT DRUG INFORMATION CENTER FEEFORSERVICE Because of the evolution in health care and the integration of new technologies, there is an increasing demand for timely, accurate, and individualized medication information. The 34 new molecular entities launched in 2011 were the most in at least 10 years.3 The U.S. drug expenditures were $320 billion for 2011, which is an increase of approximately $125 billion since 2002.2 Within the next decade health care costs will increase at an alarming rate, with total expenditures reaching the $2.1 trillion mark. A majority of these costs will be shouldered by the private sector, with a significant increase in prescription drug costs. Drug information practitioners are in an enviable position to provide a service that will improve patient outcomes and decrease health care costs through the provision of unbiased information that supports rational, cost-effective, patient- and disease-specific drug therapy. One of the best ways to deliver such information is by contracting with a drug information service with formally trained health care professionals. Potential clients include managed care groups, contract pharmacy services, federal or state government, pharmacy benefits managers, buying groups, attorneys, pharmaceutical industry, small hospitals, chain pharmacies, and independent pharmacies. In one survey, 31% of managed care organizations contracted with a drug information center.77 Several different fee structures have been used. A client may be charged a simple fee per question, or may be

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

offered a detailed menu of services (e.g., written medication evaluations) with the final cost dependent on the number and types of services chosen by the contracting party. Services provided within these contracts may include providing answers to medication information requests, preparation of new drug evaluation monographs, formulary drug class reviews, development of medication use evaluation criteria, pharmacoeconomic evaluations, guideline development for a particular disease, writing a pharmacotherapy publication (e.g., Web site, blog, newsletter), and providing continuing education programming.78 Additional services the center may make available are access to in-house question files for sharing of information on commonly asked questions, and direct access to the center’s Internet home page for review of medical use evaluations, formulary reviews, and newsletters. One center reports providing information on drug shortages to ASHP through a grant.79 Frequently, the contracting drug information center also has responsibilities for pharmacy services (e.g., medication information, drug policy) as part of an entire health system, or is based in a college of pharmacy.

MEDICAL INFORMATICS IN A HEALTH SYSTEM The majority (92%) of recently published articles on health information technology’s effect on outcomes (quality, efficiency, and provider satisfaction) reached positive conclusions.80 There are tremendous opportunities for an informatics specialist—an individual that has advanced medication information skills with a keen understanding of computer and information technology. This individual can help support patient care activities by improving the efficiency of workflow and increasing access to patient-specific information and the medical literature through technology by remote site availability. This individual may also be involved in the area of institutional drug policy management. As the integration of EHRs and CPOE continues to expand, data that were only accessible through a paper record will be available for those professionals who understand the type of data that are needed for quality improvement efforts, and are able to get information efficiently out of the system and into the hands of clinicians at the point of care.81 The role of a pharmacist as an informatics specialist has been clearly described in the successful implementation of the CPOE system in an academic medical center.82 The role goes beyond the implementation phase and includes system maintenance (e.g., formulary updates, revised clinical decision support as new guidelines and medical evidence are published, developing specialized libraries such a customized cancer chemotherapy regimen library).83

MANAGED CARE PHARMACY With total U.S. drug expenditures for pharmaceuticals increasing annually (e.g., $320 billion for 2011), this offers tremendous opportunities for the medication information

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specialist to provide leadership in the development and implementation of mechanisms to support the cost-effective selection and use of medications managed pharmacy organizations.2,77,84 With the appropriate training (e.g., specialized residency in drug information practice or managed care pharmacy) and expertise, opportunities are growing for the medication information specialist in the insurance industry, health maintenance organizations, PBM companies, state and national government agencies (e.g., Medicaid, Medicare) as well as other groups interested in the cost-efficient use of medications. A list of residencies in managed care pharmacy is available through the Academy of Managed Care Pharmacy (AMCP; http://www.amcp.org). A medication information specialist may be involved in several activities such as providing medication use evaluation assessments, encouraging the use of cost-effective alternatives, providing medication and practice-related information, managing formularies, providing information to support formulary guidelines (counterdetailing—see Chapter 23), and developing disease management program.85 Opportunities also exist to establish guidelines for selected disease states (e.g., management of patients with diabetes mellitus), or classes of drugs (e.g., selection of an appropriate antibiotic for surgical prophylaxis). Practice guidelines are becoming an increasingly important part of the biomedical literature. These clinical guidelines are systematically developed to assist practitioners and patients with decisions about health care in an effort to improve the quality and consistency of health care while minimizing costs and liability.86 Evidence-based practice guidelines are developed through systematic reviews of the literature appropriately adapted to local circumstances and values. Key questions to consider when reviewing a practice guideline have been proposed.86 These questions primarily rely on how accurately the guideline reflects the research used to produce it. More information on clinical practice guidelines can be found in Chapter 7.

POISON CONTROL Poison information is a specialized area of medication information with the practitioner typically practicing in an accredited poison information center or an emergency room. Similar to the mission of traditional drug information centers, poison information centers exist to provide accurate and timely information to enhance the quality of care of patients. There are, however, several differences between a traditional drug information center and a poison control center.87 Health professionals generate most consultations received in drug information centers, whereas, in a poison control center, most are generated from the public. Poison information centers must be prepared to provide information on management of any poison situation, including household products, poisonous plants and animals, medications, and other chemicals. Because of the type of information that the specialist provides, nearly all requests for information to a poison control center are urgent, with an average response time of 5 minutes, compared to anywhere from

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30 minutes to days for drug information centers, depending on the urgency of the call and complexity of information required. A specialist in poison information requires expertise in clinical toxicology to be able to obtain a complete history that correctly assesses the potential severity of exposure. They need to know where and how to search for this type of information and be able to develop an appropriate plan for intervention. And they need to be able to communicate the plan in a comprehensive, concise, and accurate manner to a consumer at an appropriate level of understanding. A certified specialist in poison information, or a CSPI, is a registered nurse, pharmacist, or physician who has 2000 hours of experience providing telephone poison center consultations. Recertification is required every 7 years (AAPCC; http://www.aapcc.org/). In addition to a poison control center providing patient-specific toxicology information, centers in the United States also contribute data to a larger program through the National Poison Data System (NPDS; formerly, the Toxic Exposure Surveillance System [TESS]).88 These data can be used to compare safety profiles for similar products, to develop risk assessment guidelines for specific substances, to target national poison prevention programs, and to conduct postmarketing surveillance on products (e.g., chemicals). In 2010, poison control centers received approximately four million calls. Despite the impact that regional poison control centers have on reducing morbidity and mortality with poison exposures, they are also facing increasing emphasis on economic justification. One study used a decision analysis to compare the cost-effectiveness of treating poison exposures with the services of a regional poison control center to treatment without access to any poison control center.89 The average cost per patient treated with the services of a poison control center was almost half of that achieved without services of a poison control center. These results were consistent regardless of exposure type, average inpatient and emergency department costs, and clinical outcome probabilities. Another study examined the public health cost savings by preventing unnecessary utilization of emergency department services by providing home management by a regional poison control center.90 On average, 70% of requests for poison information can be managed at home, that would otherwise need to be handled in an emergency room environment. It is estimated that a median of $33 million (range $18 million to $45 million) in unnecessary health care charges were prevented by home management by a regional poison information center in 2007. A median of approximately $36 in unnecessary health care charges were prevented for each dollar of state funding the regional poison control center received. This is a large cost savings to residents compared to dollars received in state support.

PHARMACEUTICAL INDUSTRY The pharmaceutical industry provides many career opportunities for pharmacists in a variety of areas including information technology, training and development, scientific

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communications, postmarketing research, regulatory affairs, professional affairs, medical information services, medical liaison, drug discovery, product development, and clinical research.91,92 Each area requires a different skill set, which may differ between companies, depending on the infrastructure and mission of the individual companies. A pharmacist with specialized medication information expertise or training can answer drug information questions, report and monitor adverse drug reactions, and provide information support to other departments within the pharmaceutical industry. A pharmacist, which is many times referred to as a medical liaison, can help educate health professionals about a particular group of products or provide academic support or partnership for educational initiatives. They may interact with sales and marketing, participate with regulatory affairs issues, and handle product complaints. Regulatory affairs specialists help ensure that drugs under development meet the state and federal regulations that have been developed to protect the public. Pharmacists may be called on to review adverse effects identified in clinical studies and communicate this and other information to the appropriate research and development team. In addition to providing written information on the drug product produced by the manufacturer, there are opportunities to provide additional information at pharmacy and therapeutics committees or state drug use review (DUR) boards. Pharmaceutical companies have extensive scientific data on their products; some of which is not available through other published sources or may require a formal FOI (freedom of information) request. Pharmacists with specialized drug information training can take a leadership role in evaluating current research, serving as an associate in managing ongoing research, or designing studies to help answer questions about new indications for future use of the product. The area of postmarketing research is a growing area that offers tremendous opportunity for pharmacists to share their knowledge of the health care environment, research design, technology, and economics from the perspective of the pharmaceutical industry. As the sophistication of drug products and information management (e.g., electronic new drug applications [NDAs]) has increased, so have the opportunities for pharmacists to practice in the pharmaceutical industry and focus on using the skills of a medication information specialist. Postgraduate residencies and fellowships, which are typically shared with a college of pharmacy, are available to help strengthen some of the medication information skills needed to work in the pharmaceutical industry. Other positions (e.g., in the areas of drug discovery, product development, and clinical research) require an advanced degree of study (MBA, PhD). See Chapter 22 for more information.

ACADEMIA The medication information specialist has the opportunity to provide leadership in the pharmacy curriculum, including both didactic and experiential training.73 In addition to

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

teaching medication information skills that are required across practice sites, the specialist also serves as collaborator with other faculty on cases and activity designed to reinforce drug information skills for students. Approximately one-third of drug information centers are funded by a college of pharmacy.93 This environment allows the student to be prepared to efficiently and accurately provide information to the appropriate audience, while emphasizing both didactic and competency-based experiential training. New academic opportunities for medication information training include the ACPE-required introductory pharmacy practice experiences or IPPEs (http://www.acpe-accredit.org). In this experience, students that are in their first 3 years of pharmacy school are now required to gain exposure to patient care services prior to the last year of APPEs. Answering real questions from patients and about patients may be one way to satisfy this requirement and prepare students for the more challenging mediation questions they will receive in the future. As the prevalence of evidence-based practice increases, the importance of teaching drug literature evaluation skills increases. Courses incorporating drug literature evaluation, formulary management, and the development and management of drug use policies will be best taught by drug information specialists in academia. See Chapter 21 for more information.

SCIENTIFIC WRITING AND MEDICAL COMMUNICATION Medical education and communications companies, separate from the pharmaceutical industry, may provide educational programming for health professionals and consumers to meet continuing education needs (e.g., symposia, workshops, monographs), or nonaccredited or promotional activities (e.g., sales training, publication planning, journal articles). These individuals may write, edit, or develop medication-related materials by gathering, organizing, interpreting, and presenting information for either medical professionals or the public. Examples of these materials include patient education materials, journal articles, regulatory documents, poster presentations, grant proposals, sales and marketing of pharmaceuticals, drug evaluation monographs for health systems. In addition to having good writing skills, the pharmacist also needs to have scientific expertise and literature evaluation skills.94,95 More than 77% of medical education and communication companies employ at least one licensed health care professional. These professionals may have several positions including director and scientific writer. Pharmacists in this capacity would work closely with editors, graphic designers, Web site strategists, meeting planners, and scientists. This type of information may be communicated in a variety of ways including orally, in print format, and electronically on the Web. Medical communication fellowships are available to help provide a solid foundation through experiences to various aspects of the medical communication industry.

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Summary and Direction for the Future All pharmacists must be effective medication information providers regardless of their practice site. It is one of the most fundamental responsibilities of a practitioner. Developing the skills of an effective medication information provider is the foundation for the pharmacist to be a life-long learner and problem solver. The literature is a valuable component of both of these processes and will allow the individual pharmacist to adapt to the needs of a continually changing health care system. Opportunities abound for pharmacists to use medication information skills in all practice settings either as a generalist or as a specialist practitioner. Medication information specialists will still be needed to operate the drug information centers, to provide leadership in the area of drug informatics, managed care organizations, poison control, pharmaceutical industry, scientific writing and medical communications, and in academia.

Self-Assessment Questions 1. What percentage of adverse drug events are considered to be preventable? a. 1% b. 2% c. 40% d. 95% e. 99% 2. In current practice, the term medication information is used in place of drug information a. To convey the management and use of information on medication therapy b. To prevent confusion with information on drugs of abuse c. To signify the broader role that all pharmacists take in information provision d. a and b e. a and c 3. Which of the following is true regarding complementary and alternative medicine (CAM)? a. Not used in children. b. The use of CAM has been growing nationally. c. Only 5% of hospitals have some sort of CAM option for patients. d. It is easy to find complete and accurate information on CAM. e. Drug information centers do not typically answer questions regarding CAM.

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

4. The first drug information center was opened in 1962 at a. The University of Iowa b. The Ohio State University c. The University of Kentucky d. Misr International University in Cairo, Egypt e. None of the above 5. What percentage of recently published articles on health information technology’s effect on outcomes (quality, efficiency, and provider satisfaction) reached positive conclusions? a. 15% b. 40% c. 60% d. 70% e. 92% 6. In a recent study, the cost-benefit ratio of drug information services related to patient-specific requests was found to be 2.9:1 to 13.2:1. Most savings resulted from a. Decreasing the books in the library by increasing the use of electronic sources b. Decreasing the need for drug monitoring c. Decreasing the need for additional treatment related to an adverse event d. Preventing adverse drug reactions e. b and c 7. Which is true regarding poison control centers? a. Approximately four million people contact poison information centers annually. b. Staff provide information only on ingestion of medications. c. Staff would not know how to treat snake bite. d. Staff do not require special training. e. Average response time is 30 minutes. 8. Medication information specialists’ primary leadership role(s) in the move to computerized intervention programs that automatically educate at the point of prescribing should be a. Testing the programs once they are in place b. Planning and implementing the programs c. Providing feedback to programmers on effectiveness d. Developing quality improvement programs for these systems e. None of the above

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9. For pharmacies in organized health care settings, the largest component in the pharmacy operating budget is a. Personnel b. Drug information center c. Clerical supplies d. Drugs e. Intravenous (IV) room equipment 10. A recent poll showed that of those who went on the Internet to find health information, what percentage of lay public followed up with a medical professional? a. 17% b. 20% c. 50% d. 5% e. 27% 11. What percentage of pharmacy schools offer didactic drug information education  as either a stand-alone course or an integrated course throughout the curriculum? a. 100% b. 90% c. 80% d. 75% e. 70% 12. Which of the following changes have influenced the role of the drug information specialist? a. Rise in self-care movement b. Development of evidence-based medicine c. Expansion of social media d. Focus on medication safety e. All of the above 13. The number of adverse drug events will likely increase in the future because of which of the following: a. Ongoing availability of new medications b. Growing elderly population c. Increased use of medications for disease prevention d. Improved insurance coverage for medications e. All of the above

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14. Which of the following is a registry of ongoing clinical trials? a. http://www.ClinicalTrials.gov b. http://www.newdrug.com c. http://www.pubmedcentral.nih.gov d. http://www.druginfo.com e. http://www.merck.com 15. Approximately how much did the United States spend on prescription drugs (2010)? a. $307 billion b. $508 million c. $100 billion d. $250 million e. $2 trillion

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11. Rosenberg JM, Schilit S, Nathan JP, Zerilli T. Update on the status of 89 drug information centers in the United States. Am J Health Syst Pharm. 2009;66:1718-22. 12. Pedersen CA, Schneider PJ, Scheckelhoff DJ. ASHP national survey of pharmacy practice in hospital settings: prescribing and transcribing—2010. Am J Health Syst Pharm. 2011;68:669-88. 13. Pedersen CA, Schneider PJ, Scheckelhoff DJ. ASHP national survey of pharmacy practice in hospital settings: prescribing and transcribing—2007. Am J Health Syst Pharm. 2008;65;827-43. 14. Kinky DE, Erush SC, Laskin MS, Gibson GA. Economic impact of a drug information service. Ann Pharmacother. 1999;33:11-6. 15. LaFleur J, Tyler LS, Sharma RR. Economic benefits of investigational drug services at an academic institution. Am J Health Syst Pharm. 2004;61:27-32. 16. Perez A, Doloresco F, Hoffman JM, Meek PD, Touchette DR, Vermeulen LC, Schumock GT. ACCP: Economic evaluations of clinical pharmacy services: 2001–2005. Pharmacotherapy. 2009;29(1):128. 17. Study Commission on Pharmacy. Pharmacists for the future. Ann Arbor (MI): Health Administration Press; 1975. p. 139. 18. American Society of Health-System Pharmacists. ASHP Guidelines on the provision of medication information by pharmacists. Am J Health Syst Pharm. 1996;53:1843-5. 19. American Association of Colleges of Pharmacy [Internet]. Washington, DC: Center for  the Advancement of Pharmaceutical Education; [updated 2004 May; cited 2009 Aug  22]. Available from: http://www.aacp.org/resources/education/Documents/ CAPE2004.pdf 20. Centers for Disease Control and Prevention; National Center for Emerging and Zoonotic Infectious Diseases (NCEZID); Division of Healthcare Quality Promotion (DHQP). Medication safety basics [Internet]. August 14, 2012. Available from: http://www.cdc.gov/ medicationsafety/basics.html 21. Budnitz DS, Pollock DA, Weidenbach KN, Mendelsohn AB, Schroeder TJ, Annest JL. National surveillance of emergency department visits for outpatient adverse drug events. JAMA. 2006;296:1858-66. 22. Institute of Medicine, Committee on Identifying and Preventing Medication Errors. Preventing medication errors. Washington, DC: The National Academies Press; 2006. 23. Budnitz DS, Lovegrove MC, Shehab N, Richards CL. Emergency hospitalizations for adverse drug events in older Americans. N Engl J Med. 2011;365:2002-12. 24. Wachter RM. The end of the beginning: patient safety five years after ‘To err is human’. Health Aff [Internet]. 2004 Nov 30 [cited 2009 Aug 22]. Available from: http://content. healthaffairs.org/cgi/content/full/hlthaff.w4.534/DC1 25. Ozdalga E, Ozdalga A, Ahuja N. The smartphone in medicine: a review of current and potential use among physicians and students. J Med Internet Res. 2012;14:e128. 26. Murray E, Pollack L, Donelan K, Catania J, Lee K, Zapert K, et al. The impact of health information on the Internet on health care and the physician-patient relationship: national U.S. survey among 1,050 U.S. physicians. J Med Internet Res. 2003;5:e17.

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27. Berland GK, Elliott MN, Morales LS, Algazy JI, Kravitz RL, Broder MS, et al. Health information on the Internet: accessibility, quality, and readability in English and Spanish. JAMA. 2001;285:2612-21. 28. Clauson KA, Polen HH, Boulos MN, Dzenowagis JH. Drug information: scope, completeness, and accuracy of drug information in Wikipedia. Ann Pharmacother. 2008;42:1814-21. 29. Belgado BS. Drug information centers on the Internet. J Am Pharm Assoc. 2001;41:631-2. 30. Costerison EC, Graham AS. Developing and promoting an intranet site for a drug information service. Am J Health Syst Pharm. 2008;65:639-43. 31. Dugas M, Weinzierl S, Pecar A, Hasford J. An intranet database for a university hospital drug information center. Am J Health Syst Pharm. 2001;58:799-802. 32. Ruppelt SC, Vann R. Marketing a hospital-based drug information center. Am J Health Syst Pharm. 2001; 58:1040. 33. Erbele SM, Heck AM, Blankenship CS. Survey of computerized documentation system use in drug information centers. Am J Health Syst Pharm. 2001;58:695-7. 34. Centers for Medicare and Medicaid Services. An introduction to the Medicare EHR Incentive Program for Eligible Professionals [Internet]. Available from: http://www.cms. gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/Downloads/ beginners_guide.pdf. Accessed March 20, 2013. 35. Cresswell KM, Sheikh A. Information technology-based approaches to reducing repeat  drug exposure in patients with known allergies. J Allergy Clin Immunol. 2008;121:1112-7. 36. Bond CA, Raehl CL. 2006 national clinical pharmacy services survey: clinical pharmacy services, collaborative drug management, medication errors, and pharmacy technology. Pharmacotherapy. 2008;28:1-13. 37. Rind DM, Kohane IS, Szolovits P, Safran C, Chueh HC, Barnett GO, et al. Maintaining the confidentiality of medical records shared over the Internet and world wide web. Ann Intern Med. 1997;127:138-44. 38. Frisse ME. What is the Internet learning about you while you are learning about the Internet? Acad Med. 1996;71:1064-107. 39. Giacalone RP, Cacciatore GG. HIPAA and its impact on pharmacy practice. Am J Health Syst Pharm. 2003;60:433-45. 40. Elson RB, Connelly DP. Computerized medical records in primary care their role in mediating guideline-driven physician behavior change. Arch Fam Med. 1995;4:698-705. 41. Evans RS, Pestotnik SL, Classen DC, Clemmer TP, Weaver LK, Orme JF, et al. A computer-assisted management program for antibiotics and other anti-infective agents. N Engl J Med. 1998;338:232-8. 42. Hunt DL, Haynes RB, Hanna SE, Smith K. A computer-assisted management program for antibiotics and other anti-infective agents. JAMA. 1998;280;1339-46. 43. Institute of Medicine. Health professions education: a bridge to quality. Washington, DC: The National Academies Press; 2003.

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44. Centers for Medicare and Medicaid Services. National Health Expenditure Projections 2011-2021. [Internet]. Available from: http://www.cms.gov/Research-Statistics-Data-andSystems/Statistics-Trends-and-Repor ts/NationalHealthExpendData/Downloads/ Proj2011PDF.pdf 45. Opportunities for the community pharmacist in managed care. Special Report. Washington, DC: American Pharmaceutical Association; 1994. 46. Ellrodt G, Cook DJ, Lee J, Cho M, Hunt D, Weingarten S. Evidence-based disease management. JAMA. 1997;278:1687-92. 47. Committee on Quality of Health Care in America, Institute of Medicine. Crossing the quality chasm: a new health system for the 21st century. Washington, DC: The National Academies Press; 2001. 48. Avorn J. In defense of pharmacoepidemiology—embracing the yin and yang of drug research. N Engl J Med. 2007;357(22):2219-21. 49. Vermeulen LC, Beis SJ, Cano SB. Applying outcomes research in improving the medication-use process. Am J Health Syst Pharm. 2000;57;2277-82. 50. Top 10 areas of research: report on the most popular fields of drug development. Med Ad News. 2003;137:S22. 51. Long G, Works J. Innovation in the biopharmaceutical pipeline: a multidimensional view. A report from the Analysis Group, Inc. [Internet]. January 2013. Available from: http:// www.analysisgroup.com/uploadedFiles/Publishing/Articles/2012_Innovation_in_the_ Biopharmaceutical_Pipeline.pdf. Accessed March 14, 2013. 52. Emilien G, Ponchon M, Caldas C, Isacson O, Maloteaux JM. Impact of genomics on drug discovery and clinical medicine. Q J Med. 2000;93:391-423. 53. Epler GR, Laskaris LL. Individualization health care and the pharmaceutical industry. Am J Health Syst Pharm. 2001;58:1042. 54. Frosch DL, Grande D, Tarn DM, Kravitz RL. A decade of controversy: balancing policy with evidence in the regulation of prescription drug advertising. Am J Public Health. 2010;100:24-32. 55. U.S. Government Accountability Office (GAO) report number GAO-07-54 entitled ‘Prescription drugs: improvements needed in FDA’s oversight of direct-to-consumer advertising’ which was released on December 14, 2006. Available from: http://www.gao. gov/htext/d0754.html. Accessed February 15, 2010. 56. Shiffman S, Gerlach KK, Sembower MA, Rohay JM. Consumer understanding of prescription drug information: illustration using an antidepressant medication. Ann Pharmacother. 2011;45:452-8. 57. Pew Internet and American Life Project: a project of the PewResearchCenter. Health Online 2013. January 15, 2013. Available from: http://pewinternet.org/~/media/Files/ Reports/2013/Pew%20Internet%20Health%20Online%20report.pdf. Accessed February 15, 2013. 58. Larner AJ. Use of internet medical websites and of NHS direct by neurology outpatients before consultation. Int J Clin Pract. 2002;56:219-21.

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59. Silberg W, Lundberg GD, Musacchio RA. Assessing, controlling, and assuring the quality of medical information on the Internet. JAMA. 1997;277:1244-5. 60. Wyatt J. Measuring quality and impact on the world wide web. BMJ. 1997;314:1879-81. 61. National Institutes of Health, National Center for Complementary and Alternative Medicine.. 2007 statistics on CAM use in the United States [Internet]. Washington, DC: [updated 2008 Dec; cited 2009 Aug 22]. Available from: http://nccam.nih.gov/news/ camstats/2007/camsurvey_fs1.htm 62. Barnes PM, Bloom B, Nahin R. Complementary and Alternative Medicine Use Among Adults and Children: United States, 2007. Natl Health Stat Report. 2008;10(12):1-23. 63. Complementary and alternative medicine: What people aged 50 and older discuss with their healthcare providers [Internet]. AARP and NCCAM Survey Report 2010. Available from: http://nccam.nih.gov/news/camstats/2010. Accessed March 9, 2013. 64. American Hospital Association. Latest survey shows more hospitals offering complementary and alternative medicine services [Internet].Washington, DC: cc 2006-2009. [updated  2008 Sep 15;cited 2009 Aug 22]. Available from: http://nccam.nih.gov/news/ camstats.htm 65. Ananth S, Martin W. Health forum 2005 complementary and alternative medicine survey of hospitals: summary of results. Chicago (IL): Health Forum; 2006. 66. West PM, Lodolce AE, Johnston AK. Telephone service for providing consumers with information on herbal supplements. Am J Health Syst Pharm. 2001;58:1842-6. 67. Shields KM, McQueen CE, Bryant PJ. National survey of dietary supplement resources at drug information centers. J Am Pharm Assoc. 2004;44:36-40. 68. Meade V. Patient medication information hotlines multiply. Am Pharm. 1991;NS31: 569-71. 69. Troutman WG. Consensus-derived objectives for drug information education. Drug Inf J. 1994;28:791-6. 70. Ferrill MJ, Norton LL. Drug information to biomedical informatics: a three tier approach to building a university system for the twenty-first century. Am J Pharm Ed. 1997;61:81-6. 71. Gora-Harper ML, Brandt B. An educational design to teach drug information across the curriculum. Am J Pharm Ed. 1997;61:296-302. 72. Wang F, Troutman WG, Seo T, Peak A, Rosenberg JM. Drug information in doctor of pharmacy programs. Am J Pharm Ed. 2006;70:51. 73. Bernknopf AC, Karpinski JP, McKeever AL, Peak AS, Smith KM, Smith WD, et al. Drug information: from education to practice. Pharmacotherapy. 2009;29:331-46. 74. Phillips JA, Gabay MP, Ficzere C, Ward KE. Curriculum and instructional methods for drug information, literature evaluation, and biostatistics: survey of US pharmacy schools. Ann Pharmacother. 2012;46:793-801. 75. Fox BI, Varadarajan R. Use of Twitter to encourage interaction in a multi-campus pharmacy management course. Am J Pharm Ed. 2011;75:88. 76. Lopez TC, Trang DD, Farrell NC, DeLeon MA, Villarreal CC, Maize DF. Development and implementation of a curricular-wide electronic portfolio system in a school of pharmacy. Curricular-wide use of ePortfolios. Am J Pharm Ed. 2011;75:1-6.

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77. McCloskey WW, Vogenberg FR. Drug Information resources in managed care organizations. Am J Health Syst Pharm. 1998;55:2007-9. 78. Foresster LP, Scoggin JA, Valle RD. Pharmacy management company-negotiated contract for drug information services. Am J Health Syst Pharm. 1995;52:1074-7. 79. Fox ER, Tyler LS. Managing drug shortages: seven years’ experience at one healthsystem. Am J Health Syst Pharm. 2003;60:245-53. 80. Buntin BM, Burke MF, Hoaglin MC, Blumenthal D. The benefits of health information technology: a review of the recent literature shows predominately positive results. Health Affairs. 2011;30(3):464-71. 81. Woodruff AE, Hunt CA. Involvement in medical informatics may enable pharmacists to expand their consultation potential and improve the quality of healthcare. Ann Pharmacother. 1992;26:100-4. 82. Cooley TW, May D, Alwan M, Sue C. Implementation of computerized prescriber order entry in four academic medical centers. Am J Health Sys Pharm. 2012;69:2166-73. 83. Traynor K. Pharmacy informatics aids cancer center care. Am J Health Sys Pharm. 2012;69:2125. 84. Vanscoy GJ, Gajewski LK, Tyler LS, Gora-Harper ML, Grant KL, May JR. The future of medication information practices: a consensus. Ann Pharmacother. 1996;30:876-81. 85. Taniguchi R. Pharmacy benefit management companies. Am J Health Syst Pharm. 1995;52:1915-7. 86. Field JM, Lohr KN, eds. Guidelines for clinical practice: from development to use. Washington, DC: The National Academies Press; 1992. 87. AAPCC. Annual report of the NPDS. Clin Toxicol. 2012;50:911-1164. 88. Bronstein AC, Spyker DA, Cantilena LR. 2007 Annual Report of the American Association of Poison Control Centers National Poison Data System (NPDS) 25th annual report. Clin Toxicol. 2008;46:927-1057. 89. Harrison MAJ, Draugalis JR, Slack MK, Langley PC. Cost-effectiveness of regional poison control centers. Arch Intern Med. 1996;156:2601-8. 90. Lovecchio F, Curry S, Waszolek K, Klemens J, Hovseth K, Glogan D. Poison control centers decrease emergency healthcare utilization costs. J Med Toxicol. 2008;4:221-4. 91. Gong SD, Millares M, VanRiper KB. Drug information pharmacists at health-care facilities, universities, and pharmaceutical companies. Am J Hosp Pharm. 1992;49: 1121-30. 92. Riggins JL. Pharmaceutical industry as a career choice. Am J Health Syst Pharm. 2002;59:2097-8. 93. Rosenberg JM, Kournis T, Nathan JP, Cicero LA, McGuire H. Current status of pharmacist-operated drug information centers in the United States. Am J Health Syst Pharm. 2004;61:2023-32. 94. Overstreet KM. Medical education and communication companies: career options for pharmacists. Am J Health Syst Pharm. 2003;60:1896-7. 95. Moghadam RG. Scientific writing: a career for pharmacists. Am J Health Syst Pharm. 2003;60:1899-900.

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SUGGESTED READINGS 1. Pedersen CA, Gumpper KF. ASHP national survey on informatics: assessment of the adoption and use of pharmacy informatics in U.S. hospitals—2007. Am J Health Syst Pharm. 2008;65(23):2244-64. 2. Hoffman JM, Shah ND, Vermeulen LC, Doloresco F, Grim P, Hunkler RJ, et al. Projecting future drug expenditures—2008. Am J Health Syst Pharm. 2008;65(3):234-53. 3. Hing HS, Burt CW, Woodwell DA. Electronic health record use by office-based physicians and their practices: United States, 2006. Advanced data from vital and health statistics (DHHS Publication no. (PHS) 2008-1250). No 393. Hyattsville (MD): National Center for Health Statistics; October 26, 2007:1-7. 4. Bernknopf AC, Karpinski JP, McKeever AL, Peak AS, Smith KM, Smith WD, et al. Drug Information: from education to practice. Pharmacotherapy. 2009;29:331-46. 5. Costerison EC, Graham AS. Developing and promoting an intranet site for a drug information service. Am J Health Syst Pharm. 2008;65:639-43. 6. Wang F, Troutman WG, Seo T, Peak A, Rosenberg JM. Drug information in doctor of pharmacy programs. Am J Pharm Ed. 2006;70:51. 7. Zeind CS, Blagg JD, Amato MG, Jacobson S. Incorporation of Institute of Medicine competency recommendations with doctor of pharmacy curricula. Am J Pharm Ed. 2012;76(5):article 83. 8. Shiffman S, Gerlach KK, Sembower MA, Rohay JM. Consumer understanding of prescription drug information: illustration using an antidepressant medication. Ann Pharmacother. 2011;45:452-8.

2

C hapter Two

Formulating Effective Responses and Recommendations: A Structured Approach Karim Anton Calis • Amy Heck Sheehan

Learning Objectives After completing this chapter, the reader will be able to •

•

•

•

•

Develop strategies to overcome the impediments that prevent pharmacists from providing effective responses and recommendations. Outline the steps that are necessary to identify the actual drug information needs of the requestor. List and describe the four critical factors that should be considered and systematically addressed when formulating a response. Define analysis and synthesis, and describe their applications in the process of formulating responses and recommendations. List the elements and characteristics of effective responses to medication-related queries.

Key Concepts 1 Rational pharmacotherapy can be promoted by ensuring that drug information is

correctly interpreted and appropriately applied. 2 The absence of sufficient background information and pertinent patient data can greatly

impair the process of information synthesis and the ability to formulate effective responses. 35

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3 Critical information that defines the problem and elucidates the context of the question

is not readily volunteered but must be expertly elicited. 4 Providing responses and offering recommendations without knowledge of pertinent

patient information, the context of the request, or how the information will be applied can be detrimental. 5 Formulating a response requires the use of a structured, organized approach whereby critical factors are systematically considered and thoughtfully evaluated. 6 Approaching a question haphazardly, or prematurely fixating on isolated details, can misdirect even the most skilled clinician. 7 Responses to drug information queries often must be synthesized by integrating data from diverse sources through the use of logic and deductive reasoning.

Introduction Pharmacists are asked to provide responses to a variety of drug information questions every day. Although the type of requestor, query, and setting can vary, the process of formulating responses remains constant. This chapter introduces an organized, structured approach for formulating effective responses and recommendations. As the medical literature expands, access to drug information resources by health care professionals and the public continues to grow. Yet many professionals and consumers lack the necessary skills to use this information effectively. 1 Rational pharmacotherapy can be promoted by ensuring that drug information is correctly interpreted and appropriately applied. This presents an opportunity and a challenge for bona fide drug therapy experts who aspire to play a broader role in patient care. Regardless of specialty or practice site, pharmacists with the responsibility for overseeing the safe and rational use of medications must strive to develop expertise in applied pharmacotherapy. Whether working in a community pharmacy, nursing home, outpatient clinic, hospital, or another practice site, pharmacists can apply their skills and knowledge for the optimal care of patients. Pharmacists should not be relegated to the role of information dispenser or gatekeeper, but they should instead extend their knowledge of drugs and therapeutics to the clinical management of individual patients or the care of large populations.

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ACCEPTING RESPONSIBILITY AND ELIMINATING BARRIERS Pharmacists should recognize that their responsibility extends beyond simply providing an answer to a question. Rather, it is to assist in resolving therapeutic dilemmas or managing patients’ medication regimens. Knowledge of pharmacotherapy alone does not ensure success. Moreover, isolated information is not sufficient for formulating responses to questions or ensuring proper patient management. In fact, it is uncommon to find comprehensive answers in the literature that completely and effectively address specific situations or circumstances that clinicians encounter in their daily practices. Responses and recommendations must often be thoughtfully synthesized using information and knowledge gathered from a number of diverse sources. To effectively manage the care of patients and resolve complex clinical situations, added skills and competence in problem solving and direct patient care are also necessary. In order to provide meaningful responses and effective recommendations to drug information questions, real or perceived impediments must first be overcome. One such impediment is the false perception that many drug information questions do not pertain to specific patients. Another is the perception that seemingly casual interactions with requestors and the lack of formal, written consultation somehow preclude the need for in-depth analysis and extensive involvement in patient management. Oversimplification of these interactions with requestors and failure to identify the context of the question or recognize its significance can jeopardize the clinical management of patients. 2 The absence of sufficient background information and pertinent patient data can greatly impair the process of information synthesis and the ability to formulate effective responses.

IDENTIFYING THE GENUINE NEED Historically, the approach to answering drug information queries has centered on the use of a systematic method first described by Watanabe and subsequently modified by others.1,2 This simple approach relied on the collection of basic information to document and categorize the request and to subsequently develop an organized strategy for formulating cogent responses. Although this structure remains theoretically useful from a training standpoint, if strictly applied without proper context and guidance, it has the potential to artificially fragment the process and disrupt the natural exchange of information. A documentation form (see Appendix 2–1) may be useful to guide the process of data collection and ensure that all relevant information is considered. Ultimately success will depend largely on maintaining the flow of information with minimal distractions and

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unnecessary or ill-timed questions. The goal should be to remove obstacles that may obscure the actual informational needs. This is particularly relevant in clinical settings where most queries are not purely academic or general in nature. In fact, it is rational to assume that queries from health care providers will invariably involve specific patients and unique clinical circumstances. For example, a physician who asks about the association of liver toxicity with lovastatin is probably not asking this question whimsically or out of curiosity. The physician most likely is caring for a patient who has developed signs or symptoms suggestive of hepatic impairment possibly associated with the use of this medication. Although other reasonable scenarios, albeit less likely, could have prompted this question, it would be most prudent nonetheless to consider the possibility of a patientspecific drug-induced liver injury. Even questions that are not related to patient care (refer to Case Study 2–1 as an example) must be viewed in their proper and full context. Requestors of information are typically vague in verbalizing their needs and generally provide adequate information only when specifically asked or thoughtfully prompted. Although these requestors may seem confident about their perceived needs, they may be less certain after further probing. Requestors, regardless of background, are often uncertain about the nature and extent of information that should be disclosed in order to derive the most optimal assistance. 3 Critical information that defines the problem and elucidates the context of the question is not readily volunteered but must be expertly elicited. This can be accomplished through the use of effective questioning strategies (asking logical questions in a logical sequence) and other means of information gathering that are essential for formulating informed responses. Failure of the requestor to disclose critical information or clarify the question does not obviate the need for such information or relieve the pharmacist of the duty to collect it. Although it is easy to assign blame to the requestor for failing to disclose all of the necessary information, it is ultimately the responsibility of the provider of the response to obtain information completely and efficiently. Good communication skills (both listening and questioning) are essential for gathering relevant information, discerning the real question, and identifying the genuine needs of the requestor. 4 Providing responses and offering recommendations without knowledge of pertinent patient information, the context of the request, or how the information will be applied can be detrimental. Even well-equipped drug information centers with trained staff are not immune to this problem. A study of the quality of pharmacotherapy consultations provided by drug information centers in the United States found that the centers generally failed to obtain pertinent patient data, thereby risking incorrect responses and inappropriate recommendations.3

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TABLE 21. QUESTIONS TO CONSIDER BEFORE FORMULATING A RESPONSE Are the requestor’s name, profession, and affiliation known? Does the question pertain to a specific patient? Is there a clear understanding of the question or problem? Is the correct question being asked? Why is the question being asked? Why now? Are the requestor’s expectations understood? Has pertinent patient history and background information been obtained? What are the unique circumstances that generated the query? What information is actually needed? When is the information needed and in what format (e.g., verbal, written)? How will the information provided be used or applied? How has the problem or situation been managed to date? Are there alternative explanations or management options that should be explored?

Before attempting to formulate responses, pharmacists must consider several important questions to ensure that they understand the context of the query and the scope of the issue or problem (see Table 2–1). Without this information, pharmacists risk providing general responses that do not address the needs of the requestor. More concerning, however, is that information provided without proper context can be misinterpreted or misapplied. This not only compromises credibility of the information provider, but it also can jeopardize patient care. Pharmacists must recognize the value and potential benefits of their contributions as members of the health care team. Lack of confidence in communicating with requestors can be a limiting factor. Because a telephone call from another health care provider or even a casual face-to-face interaction may not be perceived as a formal request for a consult, the significance of such apparently informal daily interactions can easily be overlooked. Interactions with physicians and other health care providers present valuable opportunities for direct involvement in patient care. The lesson often missed is that there is a fine line between a simple, seemingly general drug information question and a meaningful pharmacotherapy consultation. Knowing the context of the question, obtaining the pertinent patient data and background information, and understanding the true needs of the requestor can often be the difference. Some pharmacists are quick to answer questions without adequately understanding the context or unique circumstances from which they evolved. They focus exclusively on the answer and ignore or fail to obtain key information needed to establish the framework of the question. In essence, this can result in a correct response being provided to address an incorrect question. For example, in a question about the dose of an antibiotic, an incorrect response can be formulated and inappropriate recommendations made if one fails to

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consider such factors as the patient’s age, sex, condition being treated, end-organ function, weight and body composition, concomitant diseases (e.g., cystic fibrosis), possible drug interactions, site of infection, spectrum of activity of the antimicrobial, local bacterial resistance patterns, or other factors such as pregnancy and hemodialysis or other extracorporeal procedures. In the absence of information that provides the proper context, a question about the half-life of a medication appears rather simple. However, if the question were posed for the purpose of assisting the requestor in determining a sufficient washout period for a crossover study, one would be remiss if factors other than the half-life of the parent compound were not considered. Proper determination of a washout period would also necessitate consideration of other factors such as the activity and half-lives of known metabolites; the presence of potentially interacting medications; the effects of age, illness, and end-organ function; the persistence of pharmacodynamic effects of the medication beyond its detection in the plasma (e.g., omeprazole); and the effect of administration route on the apparent half-life (e.g., transdermally administered fentanyl). The case studies in this chapter emphasize the importance of looking beyond the initial question and recognizing that the requestor’s needs often go well beyond a superficial answer to the primary question. Pharmacists should always anticipate additional questions or concerns, including those that are not directly raised by the requestor. These questions nonetheless must be considered if a clinical situation is to be managed optimally. Case Study 2–4 considers a question about ranitidine as a possible cause of thrombocytopenia. Although the requestor may neglect to provide clarifying information or pose insightful questions that further inform this case, additional related issues and complementary questions should nonetheless be considered, as these will likely be critical in determining the ultimate success of the response (see Table 2–2). Failure to address such questions will undoubtedly result in an incorrect or inadequate response. To expertly address requests for drug information, pharmacists must also depend on their patient care skills, problem-solving skills, insight, and professional judgment. Computer databases and other specialized information sources can assist in identifying critical data, but overreliance on such resources without careful attention to pertinent background information and patient data can mislead even the most experienced clinician.

FORMULATING THE RESPONSE Building a Database and Assessing Critical Factors Formulating optimal responses requires a series of steps that must be performed completely, objectively, and in a logical sequence. The steps in the process include assembling and organizing a database of patient-specific information, gathering information about relevant disease states, collecting medication information, obtaining pertinent background

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TABLE 22. IMPORTANT QUESTIONS NOT POSED BY THE REQUESTOR Initial query posed by requestor: Can ranitidine cause thrombocytopenia? • What is the incidence of ranitidine-induced thrombocytopenia? • Are there any known predisposing factors? • Is the pathogenesis of this adverse effect understood? • How does the thrombocytopenia typically present? • Are there any characteristic subjective or objective findings? • Does thrombocytopenia due to ranitidine differ from that caused by other histamine-2 (H2)-receptor

antagonists, other medications, or other etiologies? • Is the thrombocytopenia dose related? • How severe can it become? • How soon after discontinuing the drug does it reverse (dechallenge)? • How is it usually managed? • What is the likelihood of cross-reactivity with other H2-receptor antagonists? • How risky is rechallenge with ranitidine? • Are there treatments available that can be used in place of ranitidine? • Are there alternative explanations for the thrombocytopenia in this patient (including other medications,

medication combinations, or underlying medical conditions)? • What complications, if any, can be expected?

information, and identifying other relevant factors and unique or special circumstances. 5 Formulating a response requires the use of a structured, organized approach whereby critical factors are systematically considered and thoughtfully evaluated. Table 2–3 outlines in detail the specific types of information that may need to be considered for each factor depending on the nature of the query. A list of sample background questions is found in Appendix 2–2. It should be noted that only some of this information might be pertinent for a given query or case scenario. For patient-related questions, development of a patient-specific database is one of the first steps in preparing a response. This requires the collection of pertinent information from the patient, caregivers, health care providers, and medical chart or other patient records. A comprehensive medication history is also essential. This database would invariably include information that overlaps with the medical and nursing databases. Because physicians, nurses, patients, and others often lack a clear understanding of the type of information needed for effective pharmacotherapy consultations, pharmacists must be able to identify and efficiently extract pivotal patient information from diverse sources. Once these data are collected and carefully assembled, they must be critically analyzed and evaluated in the proper context before final responses and recommendations are synthesized. Background reading on topics related to the query (e.g., diseases,

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TABLE 23. FACTORS TO BE CONSIDERED WHEN FORMULATING A RESPONSE Patient Factors Demographics (e.g., name, age, height, weight, gender, race/ethnic group, and setting) Primary diagnosis and medical problem list Allergies/intolerances End-organ function, immune function, nutritional status Chief complaint History of present illness Past medical history (including surgeries, radiation exposure, immunizations, psychiatric illnesses, and so forth) Family history and genetic makeup Social history (e.g., alcohol intake, smoking, substance abuse, exposure to environmental or occupational toxins, employment, income, education, religion, travel, diet, physical activity, stress, risky behavior, and compliance with treatment regimen) Review of body systems Medications (prescribed, nonprescription, and complementary/alternative) Physical examination Laboratory tests Diagnostic studies or procedures Disease Factors Definition Epidemiology (including incidence and prevalence) Etiology Pathophysiology (for infectious diseases consider site of infection, organism susceptibility, resistance patterns, and so forth) Clinical findings (signs and symptoms, laboratory tests, diagnostic studies)a Diagnosis Treatment (medical, surgical, radiation, biologic and gene therapies, other) Prevention and control Risk factors Complications Prognosis Medication Factors Name of medication or substance (proprietary, nonproprietary, other) Status and availability (investigational, nonprescription, prescription, orphan, foreign, complementary/ alternative) Physicochemical properties Pharmacology and pharmacodynamics Pharmacokinetics (liberation, absorption, distribution, metabolism, and elimination) Pharmacogenetics Indications (Food and Drug Administration [FDA] approved and unlabeled) continued

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TABLE 23. FACTORS TO BE CONSIDERED WHEN FORMULATING A RESPONSE CONTINUED Medication Factors (continued) Uses (diagnosis, prevention, replacement, or treatment) Adverse effects Allergy Cross-allergenicity or cross-reactivity Contraindications and precautions Effects of age, organ system function, disease, pregnancy, extracorporeal circulation, or other conditions or environments Mutagenicity and carcinogenicity Effect on fertility, pregnancy, and lactation Acute or chronic toxicity Drug interactions (drug–drug or drug–food) Laboratory test interference (analytical or physiologic effects) Administration (routes, methods) Dosage and schedule Dosage forms, formulations, preservatives, excipients, product appearance, delivery systems Monitoring parameters (therapeutic or toxic) Product preparation (procedures, methods) Compatibility and stability Pertinent Background Information, Special Circumstances, and Other Factors Setting Context Sequence and time frame of events Rationale for the question Event(s) prompting the question Unusual or special circumstances (including medical errors) Acuity and time constraints Scope of question Desired detail or depth of response Limitations of available information or resources Completeness, sufficiency, and quality of the information Applicability and generalizability of the information a

Factors such as disease or symptom onset, duration, frequency, and severity must always be carefully assessed.

medications, and laboratory tests) is often essential (see Chapter 3). This process also often involves careful evaluation of the literature (see Chapters 4 and 5). To effectively perform the steps outlined previously, one must begin with a broad perspective (i.e., observing the big picture) to avoid losing sight of important information. 6 Approaching a question haphazardly, or prematurely fixating on isolated details, can misdirect even the most skilled clinician.

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Analysis and Synthesis Analysis and synthesis of information are among the most critical steps in formulating responses and recommendations. Together they assist in forming opinions, arriving at judgments, and ultimately drawing conclusions. Analysis is the critical assessment of the nature, merit, and significance of individual elements, ideas, or factors. Functionally, it involves separating the information into its isolated parts so that each can be critically assessed. Analysis requires thoughtful review and evaluation of the quality and overall weight of available evidence. Although this process involves consideration of all relevant positive findings, pertinent negative findings should not be overlooked. Once the information has been carefully analyzed, synthesis can begin. Synthesis is the careful, systematic, and orderly process of combining or blending varied and diverse elements, ideas, or factors into a coherent response. 7 Responses to drug information queries often must be synthesized by integrating data from diverse sources through the use of logic and deductive reasoning. This process relies not only on the type and quality of the data gathered, but also on how these data are organized, viewed, and evaluated. Synthesis, as it relates to pharmacotherapy, involves the careful integration of critical information about the patient, disease, and medication along with pertinent background information to arrive at a judgment or conclusion. Synthesis can give existing information new meaning and, in effect, create new knowledge. The use of analysis and synthesis to formulate a response is akin to assembling a jigsaw puzzle. If the pieces are identified and then grouped, organized, and assembled correctly, the image will be comprehensible. However, if too many of the pieces are missing—as may be the case if patient information or supporting evidence is incomplete or absent—or are not arranged correctly (e.g., when information is not evaluated, interpreted, or applied logically), formulating a cogent response may prove to be difficult or altogether impossible.

Responses and Recommendations An effective response obviously must adequately address and answer the question. Other characteristics of effective responses and recommendations are outlined in Table 2–4. The response to a question must include a restatement of the request and clear identification of the problems, issues, and circumstances. The response should begin with an introduction to the topic and systematically present the specific findings. Pertinent background information and patient data should be succinctly addressed. Conclusions and recommendations are also included in the response along with pertinent reference citations. The format of responses (verbal or written) is discussed in Chapter 9. In formulating responses, one should disclose the available information that is most relevant to the question and present all reasonable options and alternatives along with an explanation and evaluation of each. Specific recommendations must be scientifically sound, clearly justified, and well documented. A carefully worded record of the response must be maintained for follow-up

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TABLE 24. DESIRED CHARACTERISTICS OF A RESPONSE Timely Current Accurate Complete Concise Supported by the best available evidence Well-referenced Clear and logical Objective and balanced Free of bias or flaws Applicable and appropriate for specific circumstances Answers important related questions Addresses specific management of patients or situations

and for legal reasons. The records may be confidentially maintained in a patient’s chart or in the provider’s secure files.

Follow-Up When recommendations are made, follow-up should always be provided in a timely manner. Follow-up is required for assessment of outcomes and, when necessary, to reevaluate the recommendations and make appropriate modifications; it is also a hallmark of a true professional and demonstrates a commitment to patient care. Furthermore, follow-up allows the provider of the information to know if their recommendations were accepted and implemented. Finally, follow-up also allows the provider to receive valuable feedback from other clinicians and to learn from the overall experience.

Conclusion Formulating effective responses and recommendations requires the use of a structured, organized approach whereby critical factors are systematically considered and thoughtfully evaluated. The steps in this process include organizing relevant patient data, gathering information about the disease states and affected body systems, collecting medication information, obtaining pertinent background information, and identifying other relevant factors that can potentially influence outcomes. Once these data are collected and carefully assembled, they must be critically analyzed and evaluated in the proper context of each unique case. Responses and recommendations are synthesized by integrating information and evidence from diverse sources through the use of logic and deductive reasoning.

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Case Study 2–1

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INITIAL QUESTION

What is the molecular weight of enalapril?

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POTENTIAL RESPONSE IN THE ABSENCE OF RELEVANT BACKGROUND INFORMATION

Enalapril is an oral angiotensin-converting enzyme inhibitor (ACE-I) that is indicated for the management of hypertension, symptomatic congestive heart failure, and asymptomatic left ventricular dysfunction.4,5 The molecular weight of enalapril is 376.45.6

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PERTINENT BACKGROUND INFORMATION

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PERTINENT PATIENT FACTORS

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PERTINENT DISEASE FACTORS

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PERTINENT MEDICATION FACTORS

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ANALYSIS AND SYNTHESIS

The requestor is a basic scientist who is conducting an in vitro experiment to evaluate the pharmacologic effects of enalapril. She would like to know the molecular weight of enalapril so that she can perform appropriate calculations specified for this experiment.

N/A

N/A

Enalapril is a prodrug that is converted in vivo to the pharmacologically active form, enalaprilat.4,5 Both enalapril and enalaprilat are commercially available for use in the United States.

Considering that enalapril is a prodrug that must be converted to a pharmacologically active compound in vivo, and given that this researcher wishes to conduct an in vitro

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study, the researcher should use the active form of the drug in the experiment. Therefore, she should have requested the molecular weight of enalaprilat.

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RESPONSE AND RECOMMENDATIONS

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CASE MESSAGE

Enalapril is an oral angiotensin-converting enzyme inhibitor that is indicated for the management of hypertension, symptomatic congestive heart failure, and asymptomatic left ventricular dysfunction. Because enalapril is a prodrug that requires conversion to the active form, the requestor was advised to consider using enalaprilat in the experiment. The molecular weight of enalaprilat is 384.43.6

This example illustrates the importance of collecting pertinent background information, even for seemingly uncomplicated questions. Failure to understand exactly how the information that is provided will be used could result in an inaccurate or misleading response. In this case, providing the molecular weight without alerting the requestor that enalapril is pharmacologically inactive in vitro, would have resulted in wasted time and money, and the results of the experiment would likely have been invalid.

Case Study 2–2

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INITIAL QUESTION

What is the maximum dose of oprelvekin?

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POTENTIAL RESPONSE IN THE ABSENCE OF RELEVANT BACKGROUND INFORMATION

The recommended dose of oprelvekin in adult patients is 50  μg/kg given once daily.7 Larger doses of oprelvekin (75 to 100 μg/kg/day) have been studied in patients with breast cancer.8 Constitutional symptoms associated with oprelvekin therapy, such as myalgias, arthralgias, and fatigue, were noted to increase in a dose-dependent fashion. One patient who received 100 μg/kg/day of oprelvekin experienced a cerebrovascular event after the third dose. Dose escalation greater than 75 μg/kg/day was discontinued in this study, and the maximum tolerated dose of oprelvekin was determined to be 75 μg/kg/day.8

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PERTINENT BACKGROUND INFORMATION

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PERTINENT PATIENT FACTORS

The requestor is a physician who is managing a patient with human T-cell leukemia/ lymphoma virus Type I (HTLV-1)–associated adult T-cell leukemia. The patient received myelosuppressive chemotherapy and subsequently developed prolonged and severe thrombocytopenia. Oprelvekin was prescribed in an attempt to improve the patient’s platelet count and allow continuation of therapy. After 4 days of oprelvekin therapy at a dose of 50 μg/kg/day, the patient’s platelet count did not increase substantially. The physician would like to know if doses greater than 50 μg/kg/day of oprelvekin have been studied. She is planning to increase the patient’s dose to achieve a better response.

R.R. is a 70-year-old man with HTLV-1–associated adult T-cell leukemia who has been treated with zidovudine plus interferon α-2b and four cycles of cyclophosphamide, hydroxydaunomycin (doxorubicin), vincristine (Oncovin®), and prednisone, the combination of which is referred to as CHOP. After these treatments, R.R. developed severe and protracted thrombocytopenia, which has prevented further treatment.

Past Medical History HTLV-1 adult T-cell leukemia • Cardiomegaly (ejection fraction 28%) secondary to zidovudine (AZT) and interferon α-2b treatment • Peptic ulcer disease • Hypertension • Thrombocytopenia •

Social History Ø alcohol • Ø tobacco •

Current Medications Oprelvekin 50 μg/kg/day subcutaneously Pantoprazole 40 mg orally daily • Ramipril 5 mg orally daily • Trimethoprim-sulfamethoxazole one double-strength tablet orally daily • Dexamethasone 40 mg orally daily • Loperamide 4 mg orally as needed for diarrhea • Acetaminophen 325 mg orally as needed for headache • Ø complementary/alternative or other nonprescription medications • •

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Allergies/Intolerances No known drug allergies

Laboratory Results Sodium 135 mmol/L, potassium 4.9 mmol/L, chloride 103 mmol/L, CO2 22 mmol/L, creatinine 1.1 mg/dL, glucose 91 mg/dL, blood urea nitrogen (BUN) 25 mg/dL, albumin 3 g/dL, calcium (total) 2.49 mmol/L, magnesium 0.75 mmol/L, phosphorus 3.4 mg/dL, liver function tests (LFTs) within normal limits, white blood cells (WBCs) 28.3 × 109/L, hemoglobin (Hgb) 10.1 g/dL, hematocrit (Hct) 28.1% Date 7/13 7/14a 7/15 7/16 7/17 a

Platelet Count 25 K/mm3 21 K/mm3 26 K/mm3 29 K/mm3 28 K/mm3

Day 1 of oprelvekin therapy.

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PERTINENT DISEASE FACTORS

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PERTINENT MEDICATION FACTORS

It is not known whether patients with adult T-cell leukemia respond differently to oprelvekin than those with other types of nonmyeloid malignancies.

Oprelvekin, or recombinant interleukin-11, is indicated for the prevention of severe thrombocytopenia and the reduction of the need for platelet transfusions following myelosuppressive chemotherapy in adult patients. The U.S. Food and Drug Administration (FDA)-approved dose of oprelvekin is 50 μg/kg once daily for up to 21 days.7 Larger doses of oprelvekin (75 to 100 μg/kg/day) have been studied in patients with breast cancer.7,8 Constitutional symptoms associated with oprelvekin therapy, such as myalgias, arthralgias, and fatigue, were noted to increase in a dose-dependent fashion. One patient who received 100 μg/kg/day of oprelvekin experienced a cerebrovascular event after the third dose. Dose escalation greater than 75 μg/kg/day was discontinued in this study, and the maximum tolerated dose of oprelvekin was determined to be 75 μg/kg/day.8 However, the manufacturer warns that doses greater than 50 μg/kg/day may be associated with an increased incidence of fluid retention and cardiovascular events in adult patients.4 After initiation of therapy, platelet counts usually begin to increase between 5 and 9 days, with peak counts occurring after about 14 to 19 days of therapy.8

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ANALYSIS AND SYNTHESIS

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RESPONSE AND RECOMMENDATIONS

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CASE MESSAGE

Because R.R. has only received 4 days of oprelvekin treatment and platelet counts are expected to increase between 5 and 9 days after the initiation of therapy, adequate time for an optimal response to oprelvekin therapy has not been reached. In addition, oprelvekin doses greater than 75 μg/kg/day have been associated with serious adverse effects in adult patients. Therefore, increasing the dose of oprelvekin in this patient is probably not necessary, and may increase the risk of serious adverse effects without providing additional therapeutic benefits.

Oprelvekin, or recombinant human interleukin-11, is a thrombopoietic growth factor that stimulates the proliferation of hematopoietic stem cells and megakaryocyte progenitor cells, resulting in increased platelet production. Oprelvekin is indicated for the prevention of severe thrombocytopenia in patients with nonmyeloid malignancies who are at high risk for severe thrombocytopenia following chemotherapy.7 Platelet counts usually begin to increase between 5 and 9 days after initiation of oprelvekin, with peak platelet counts occurring after 14 to 19 days of therapy.7,8 R.R. has only received 4 days of oprelvekin treatment, which is insufficient for an optimal response. In addition, the adverse effects of oprelvekin therapy (e.g., myalgias, arthralgias, fatigue, fluid retention, and cardiovascular events) are dose dependent.7,8 Therefore, increasing the oprelvekin dose at this time is not warranted. In fact, doing so may predispose the patient to an increased risk of adverse effects without the prospect of added therapeutic benefit.

This example demonstrates the importance of understanding the proper context of the query. In this case, the physician is asking the wrong question. The pharmacist must collect critical background information to determine the actual drug information needed. Had the pharmacist failed to collect pertinent patient information, the physician may have increased the dose of the medication after being told that doses of 75  μg/kg/day of oprelvekin have been used. This would have been inappropriate, given that this patient had not received the medication for a sufficient duration to achieve optimal response. Moreover, larger doses of this medication are associated with a higher incidence of adverse effects.

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Case Study 2–3

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INITIAL QUESTION

Are there any drug interactions between labetalol, clonidine, amlodipine, lorazepam, and minoxidil?

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POTENTIAL RESPONSE IN THE ABSENCE OF RELEVANT BACKGROUND INFORMATION

An extensive search of tertiary4,9-12 and secondary literature sources did not reveal any significant drug–drug interactions between labetalol, clonidine, amlodipine, lorazepam, and minoxidil. However, concomitant therapy with a β-adrenergic antagonist, an α-adrenergic antagonist, a calcium channel antagonist, and a peripheral vasodilator may increase the potential for additive hypotension.

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PERTINENT BACKGROUND INFORMATION

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PERTINENT PATIENT FACTORS

The requestor is a physician who is caring for a patient with severe hypertension. The physician plans to add minoxidil to the antihypertensive regimen because the patient’s morning blood pressure is not optimally controlled. He would like to make sure that there are no drug interactions between minoxidil and the patient’s other medications.

S.L. is a 40-year-old man with severe hypertension and renal insufficiency.

Past Medical History HIV infection × 5 years • Hepatitis C × 8 years • Hypertension × 4 years • Renal dysfunction •

Social History 1 to 2 pints of vodka daily × 12 years • 1 pack per day (PPD) of cigarettes × 25 years • History of intravenous drug abuse •

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Current Medications Labetalol 400 mg orally daily (@9 AM) Clonidine transdermal patch 0.3 mg/day • Amlodipine 10 mg orally daily (@9 AM) • Lorazepam 1 mg orally as needed for anxiety • Multiple vitamin orally daily • Ø complementary/alternative or other nonprescription medications • •

Allergies/Intolerances •

Lisinopril (angioedema)

Laboratory Results Sodium 136 mmol/L, potassium 4.7 mmol/L, chloride 102 mmol/L, CO2 24 mmol/L, creatinine 2.9 mg/dL, glucose 98 mg/dL, BUN 14 mg/dL • Viral DNA 140 mmHg) while just below this value for the celecoxib (i.e., 141 versus 139 mmHg, respectively). The absolute difference between these two blood pressure values is minimal (2 mmHg), but the number of subjects counted as hypertensive is different (6%). The change in SBP between these two medications does not appear to be clinically different. Even though the measured endpoints between subjects randomized to the intervention are numerically close to the control, the subjects were categorized differently based on cut-off blood pressure values. This example illustrates the potential biases of this form of data analysis and presentation. The foundation in determining clinical difference between an intervention and control is the p-value and actual study results. Other items can assist the reader in this endeavor and include 95% confidence intervals (CI) and calculating measures of association; both of these are described below. However, readers must remember that not all clinical trials may have these latter two items. Thus, the importance of analyzing and interpreting p-values and the magnitude of difference between the intervention and control cannot be overemphasized.

Confidence Intervals Many clinical trials include 95% CI with the study results, which can assist in assessing clinical difference between the intervention and control. 9 The use of 95% CI can assist the reader in assessing the magnitude of difference in effect between the intervention and control to apply to the population. CI provide data that address the size of effect (e.g., mean reduction in DBP) of the intervention under investigation in a clinical trial by presenting a range that likely covers the true but unknown value.150,151 Although the basis of

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accepting or rejecting H0 is based on the p-value, a limitation of the p-value is that the magnitude of difference in effect between the intervention and control groups of a clinical trial is not known, since it is not able to be determined based on a statistical calculation.120,147 Because of this, the use of a CI can assist in judging the clinical usefulness of the study result.114 Clinical trials report the effect of an intervention as a point estimate, a single value that can be considered to represent the true effect (e.g., mean reduction in DBP; incidence of MI). For instance, an angiotensin-converting enzyme (ACE) inhibitor lowered mean DBP by 8 mmHg; this value would be termed the point estimate. If the study was repeated, a similar, but not exact, reduction in mean DBP may occur (e.g., −10 mmHg, −12 mmHg). The presentation of the results only as a point estimate provides the reader with limited information. Clinical trials presenting 95% CI in conjunction with the point estimate enables the readers to further critique the study results and determine the usefulness for practice. A CI provides an indication of the outcome within the population and is interpreted as a range of values in which the true value is included. The 95% CI for an average is calculated using the SEM from the trial sample. Recalling the formula for SEM, SD is divided __ by the square root of the sample size (SD/√n ). A 95% CI is equivalent to approximately two SEMs from the sample mean, with an exact formula: CI = mean ±1.96 * SEM. SEM is used as opposed to SD since SEM is more reflective of the population variance, while SD is indicative of the dispersion within the sample.120,151 A 95% CI is not the only CI reported in the literature, and readers of clinical trials need to recognize the changes in the interpretation. A 99% CI indicates more confidence that the true, but unknown, endpoint value is in this range than does a 95% CI. Thus, the 99% CI range is wider in value than a 95% CI, whereas a 90% CI range is narrower (i.e., less confident).151,152 A 95% CI for a point estimate is a common method of data presentation. Investigators of a clinical trial reported the mean reduction in DBP with an ACE inhibitor was −11.3 mmHg (95% CI, −8.2 to −14.4 mmHg) in subjects with a mean baseline DBP of 99 mmHg. This indicates that the investigators are 95% confident that the mean DBP reduction in the population is between −8.2 and −14.4 mmHg. An important issue to recognize in interpreting a 95% CI is a lower probability for the mean reduction in DBP at the upper and lower ends of the 95% CI range compared to numbers near the point estimate value.123 The further away a value lies from the point estimate within the 95% CI range, the lower the probability that this value is representative of the given population. A low probability exists that the ACE inhibitor lowers mean DBP in the population by only −8.2 mmHg compared to a higher probability that mean DBP reduction is closer to the point estimate of 11.3 mmHg (the same is true with the upper end of the 95% CI). Within this trial, the ACE inhibitor was compared to hydrochlorothiazide (HCTZ) and the mean DBP reduction with HCTZ was −9.9 mmHg (95% CI, −7.5 to −13.3 mmHg). The same principles are used to interpret this 95% CI as with the ACE inhibitor 95% CI; the

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investigators are 95% confident that the mean reduction of DBP in the population with HCTZ is between 7.5 and 13.3 mmHg. In addition, these two 95% CI ranges can be compared to determine any difference in effect between these two agents. Since both 95% CI ranges overlap considerably, no difference in effect is concluded.114,120,152 However, if no overlap of the 95% CI for the two groups is present, a clinical difference can be concluded. Another common method of data presentation is calculating a 95% CI for the difference of the point estimates between two groups. In the above trial example, the point estimate of mean DBP lowering with the ACE inhibitor was −11.3 mmHg while −9.9 mmHg for HCTZ. The difference in mean DBP between these two equals −1.4 mmHg (−11.3 minus −9.9 = −1.4). The 95% CI for the difference in the point estimates is calculated to be −3.9 to +1.1 mmHg. This is interpreted as being 95% confident that the difference in mean DBP reduction can be 1.1 mmHg greater with HCTZ (i.e., −13.1 mmHg for HCTZ versus −12 mmHg for ACE inhibitor) or 3.9 mmHg greater with the ACE inhibitor (i.e., −16.9 mmHg for ACE inhibitor versus −13 mmHg for HCTZ). Notice the upper end of the 95% CI of the difference in point estimates is a positive number (+1.1 mmHg). This does not indicate the mean DBP was increased, only that the difference in mean DBP lowering was 1.1 mmHg greater with HCTZ compared to ACE inhibitor (i.e., −12 minus −13.1 mmHg for ACE inhibitor and HCTZ, respectively). Also, in this 95% CI is the number zero (value of equality); this indicates with 95% confidence that there is no difference in mean DBP between these two groups (i.e., −13.2 minus −13.2 mmHg for both agents equals zero). If no zero is in the 95% CI of the difference between the two point estimates, then a clinical difference in effect between the intervention and control could be concluded. The interpretation of clinical difference using 95% CI is dependent on clinical experience and appropriate assessment. A 95% CI without a zero in the range does not always indicate a clinical difference between the intervention and control. For example, a 95% CI for mean DBP lowering in a trial comparing an ACE inhibitor and HCTZ was −1.9 to −0.5 mmHg. Even though this 95% CI range does not contain a zero, a mean difference of only 0.5 to 1.9 mmHg greater DBP lowering effect with one agent would not be considered clinically different.

Interpreting Risks and Number Needed to Treat Another technique to critique and interpret clinical trial results is to calculate the measures of association: relative risk (RR), relative risk reduction (RRR), absolute risk reduction (ARR), and number needed to treat (NNT). 10 Calculating measures of association (RR, ARR, RRR, NNT) for nominal data provides further information to evaluate the meaning of controlled clinical trial results. However, these calculations can only be performed with clinical trials designed to determine if there is a reduction in an outcome that occurs with modification of a risk factor when comparing the intervention to the

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TABLE 48. PRESENTING NOMINAL DATA STUDY RESULTS Adverse Event Group

Yes

No

Intervention Control

A C

B D

control. Examples of outcomes could include the incidence of MI, stroke, hospitalization, or death. Since the endpoint is dichotomous (i.e., occurred or did not occur), the results can be set up in a table, as illustrated in Table 4–8. As seen from Table 4–8, the subjects randomized to the intervention are represented by either A (number of subjects experiencing the outcome) or B (those without the outcome). Subjects assigned to the control group and experiencing the outcome are designated by C while those without the outcome by D.152,153 Table 4–9 displays the formulas to calculate the four measure of association values plus provides a description of these measures.152,153 A description of interpreting these values follows. RR is calculated as the proportion of the intervention group experiencing the outcome divided by the proportion of the control group with the event. RR = 1 indicates no difference between the intervention and control (i.e., the incidence of the outcome was not increased or decreased with the intervention compared to control). Anytime a numerator divided by a denominator calculates to 1, these two variables are equal. RR < 1 signifies the intervention lowered the risk of the outcome compared to the control (i.e., protective effect); a lower proportion of the intervention group compared to TABLE 49. MEASURES OF ASSOCIATION DESCRIPTION AND FORMULAS Measure of Association

Description

Formula

RR

Amount of risk removed by the intervention compared to control

RRR ARR

Percent of baseline risk removed Percentage of subjects treated with the intervention spared the adverse outcome compared with the control

NNT

Number of subjects needed to be treated to prevent one adverse event. A time course is included that represents the average (or median) duration of follow-up during the trial

[A/(A + B)]/[C/(C + D)]; in other words, (% of intervention group with primary endpoint) / (% of control group with primary endpoint) 1 – RR [C/(C + D)] – [A/(A + B)]; in other words, (% of control group with primary endpoint) – (% of intervention group with primary endpoint) 1/ARR

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the control experienced the outcome. RR > 1 indicates the intervention increased the risk of the outcome; a greater proportion of the intervention group had the outcome compared to control. As an example, RR of death equal to 0.70 was reported in a clinical trial in which subjects were randomized to either simvastatin (n = 2221) or placebo (n = 2223).76 RR was calculated by dividing the proportion of the subjects who died taking simvastatin (n = 182) by the proportion of those who died taking placebo (n = 256). The calculation of RR for this trial is: (182/2221)/(256/2223). RR is < 1, which indicates simvastatin lowered the risk of death by almost one-third of the baseline risk compared to placebo. RRR indicates the relative change in the outcome rate between the intervention and control groups. RRR was calculated as 30% ((1 − 0.70)*100); Thus, the risk of experiencing death was 30% lower by treating these subjects with simvastatin instead of placebo. ARR refers to the difference in the outcome rate between the intervention and control groups. A higher proportion of subjects taking placebo died (n = 256 of 2223 or 11.5%) compared to those taking simvastatin (182 of 2221 or 8.2%). ARR for death associated with simvastatin in this trial equals 3.3% (ARR = 11.5%-8.2%); thus, 3.3% of the subjects receiving simvastatin were spared death compared to placebo. NNT of this study equals 30 (NNT = 1/0.033), meaning 30 subjects need to be treated for a median of 5.4 years with simvastatin instead of placebo to prevent one case of death. The trial had a median follow-up time period of 5.4 years. Many clinical trials present an endpoint as a relative change, which can be a misleading value. For instance, RRR of stroke associated with atorvastatin was 48% compared to placebo in this study. Although this value appears very beneficial to subjects at risk for stroke, ARR needs to be evaluated besides just the RRR value. The actual incidence of stroke was 1.5% (21 of 1428 subjects treated with atorvastatin) versus 2.8% (39 of 1409 subjects treated with placebo), which calculates to an absolute difference of 1.3% (ARR). Even though almost 50% less subjects (a relative difference) experienced a stroke with atorvastatin, this represents only a difference of 18 in a group of just over 2800 subjects.154 All four measures of association can be calculated for clinical trials measuring nominal data and assessed together for the reader to determine the clinical difference in effect between the intervention and control. As seen by the simvastatin example above (in addition Table 4–9), the same study result (e.g., death) can be presented using four different methods with different meanings. However, readers should not be misled by clinical trials that only present and discuss one of these values, which usually is the most appealing value (i.e., the one that seems to show the greatest difference). In fact, studies have documented that practitioners are more inclined to select a therapy presented as RRR more often than if the same study result was presented as all four values (i.e., ARR, RR, RRR, NNT).155 Thus, investigators may be biased and selectively present the most appealing of these four values to mislead the reader into concluding a greater difference in effect between the intervention and control, even though the difference may be minimal.

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CI can also be calculated for nominal endpoints presented as RR or hazard ratio (HR). The latter is used to describe risks associated with adverse events and/or mortality data. The same formula for RR is used to calculate HR, which refers to whether the hazard of the adverse event (i.e., MI, hospitalization) is lowered or increased with the intervention compared to the control.156 According to the formula for RR (and HR), a calculated value of 1 signifies the incidence of the adverse event is equal between the intervention and control (i.e., numerator and denominator are equal, therefore, no difference).114,152 As previously mentioned, RR < 1 signifies the intervention lowered the risk and RR > 1 is interpreted as the intervention increasing the risk of the adverse event compared to the control. Therefore, investigators of a clinical trial presenting RR (or HR) with a 95% CI that lies entirely on one side of 1 (i.e., up to 0.99 or 1.01 and upward) indicates a difference in effect between the intervention and control. The 95% CI range for death in the simvastatin study was entirely below 1 (0.58 to 0.85)76, which is interpreted as the investigators are 95% confident that RR associated with simvastatin is between 0.58 and 0.85 for the population. Since 1 is not in this range, the investigators are 95% confident that RR of experiencing the adverse event is reduced with simvastatin (i.e., difference in effect). Using another example, the calculated HR for the primary endpoint of CHD was 1.82 with a 95% CI of 1.49 to 2.01. This information indicates the investigators are 95% confident that the risk of CHD is increased with the intervention versus placebo in the population since HR is > 1. Also, the investigators were 95% confident that the intervention increased CHD risk in the population since the 95% CI for this endpoint did not include 1. However, a 95% CI containing the value of 1 indicates the intervention may have neither lowered nor increased the risk (or hazard) of the adverse event. For instance, HR for death due to other causes was calculated as 0.92 (95% CI, 0.74 to 1.14). The 95% CI range lies on both sides of 1 and indicates the risk of death could be lowered to 0.74 or increased to 1.14 with the intervention. Thus, the investigator (or reader) would conclude that the intervention is no different than placebo in decreasing or increasing the risk of death.

Case Study 4–1 Your uncle (67 years of age) who has nonvalvular atrial fibrillation and diabetes contacts you for your opinion on the newly approved anticoagulant medication apixaban (Eliquis®). He read an advertisement in a magazine indicating that use of apixaban led to a significant reduction in ischemic stroke, hemorrhagic stroke, and systemic embolization. He is well controlled on warfarin therapy for over 20 years with no bleeding events or other serious adverse effects and is wondering if he should switch to apixaban due to claims of being more effective than warfarin to prevent ischemic stroke secondary to systemic embolism. Other than age and medication history, he has no other risk factors for bleeding. You

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locate the advertisement your uncle references and determine that the data presented are extracted from the trial that is summarized below. Use the summary provided below to assist your uncle in making an informed decision before visiting his cardiologist. A double-blind trial assessed the efficacy and safety of apixaban to warfarin in subjects with atrial fibrillation (A-fib). Individuals from 39 countries were randomized to either apixaban 5 mg twice daily (n = 9120) or adjusted-dose warfarin (target INR, 2 to 3; n = 9081). Inclusion criteria included A-fib plus one additional risk factor for stroke, age ≥ 75 years, previous stroke or TIA, previous systemic embolism, or symptomatic HF within 3 months or left ventricular ejection fraction of no more than 40%. Exclusion criteria included A-fib due to a reversible cause, moderate or severe mitral valve stenosis, conditions other than A-fib requiring anticoagulation, and stroke within the previous 7 days. Baseline demographics (e.g., mean age, female:male ratio) were similar in both groups with no statistical differences. The subject mean age was 70 years, approximately, 25% of the subjects resided in North America, and the mean CHADS2 score was 2.1 ± 1.1 in both groups. The median follow-up duration was 1.8 years. The primary efficacy outcome was occurrence of stroke or systemic embolism, and the secondary efficacy outcome was death from any cause. The primary outcome (via ITT) occurred in 212 and 265 subjects receiving apixaban and warfarin, respectively (HR = 0.79; 95% CI, 0.66 to 0.95; p = 0.01). The study investigators concluded that apixaban is more efficacious than warfarin in this patient type. 1. 2. 3. 4. 5.

Calculate the RRR for the primary endpoint and interpret this value. Calculate the ARR for the primary endpoint and interpret this value. Calculate the NNT for the primary endpoint and interpret this value. Interpret the CI for the primary endpoint: HR = 0.79, 95% CI, 0.66 to 0.95. As you read the clinical results for the different types of stroke, you locate information for ischemic stroke and systemic embolization (the events of greatest concern for your uncle):

“Ischemic stroke or uncertain type of stroke occurred in 199 and 250 subjects in the apixaban group and warfarin group, respectively (HR = 0.92, 95% CI, 0.74-1.13; p = 0.42). Furthermore, systemic embolization occurred in 15 and 17 subjects in the apixaban and warfarin groups, respectively (HR = 0.87, 95% CI, 0.44-1.75; p = 0.70).” What would your evidence-based response to your uncle be based on the information presented?

No Difference Does Not Indicate Equivalency Using the above items (e.g., 95% CI, measures of association) assists the reader in determining the clinical significance of study results with p-values less than α-values.

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Remember, study results are not automatically clinically different (or significant) with p-values less than α-values. However, clinical studies reporting p-values greater than the α-value translate into no statistical significance; thus, no clinical difference in effect is declared between the intervention and control groups. H0 is accepted (fail to be rejected) and H1 is rejected; in response, the statement of no difference is accepted.119,146 H0 is not written to state the intervention and control are the same, but stated as no difference in the effect (i.e., endpoint measurement) between the intervention and control. 11 Nonstatistically significant results do not equate to the intervention and control being the same or equal. Studies accepting H0 have the possibility of a Type II error. A difference in effect between the intervention and control groups may be present, but either by chance or a small trial sample size the difference in effect was not detected. In this latter instance, the clinical trial may not have been powered sufficiently to detect the difference. Usually (but not all of the time) clinical trials in which H0 is accepted have too small of a sample size.114 In fact, some studies may even be designed with an insufficient sample size so the investigators may claim equivalence between the intervention and active control after rejecting H0 even though a trial with an appropriate sample size could detect a difference. For instance, a study comparing the blood pressure lowering effects of a new ACE inhibitor to a highly prescribed ACE inhibitor may use a small sample size to obtain study results that are not statistically different. Unfortunately, the trial results may be incorrectly interpreted by some readers as both ACE inhibitors being the same and/or the new ACE inhibitor being promoted as being equal as the comparative ACE inhibitor. This situation can occur in biased articles and/or presentations. However, the correct interpretation should be no difference detected. As one author stated, “absence of evidence is not evidence of absence.”113

Assessing the Clinical Relevance of the Results 12 All controlled clinical trial results need to be assessed to determine the clinical relevance (i.e., meaningfulness) of the intervention versus control.31,122,147 In other words, what do these results mean to practice? Small treatment effects and/or differences may be statistically different, but do not really mean much clinically.61,80,114 For example, an antihypertensive medication lowered mean DBP by 5 mmHg versus 2 mmHg for placebo (p = 0.04). H0 was rejected due to statistical difference. However, mean baseline DBP was 98 mmHg and this antihypertensive medication only lowered mean DBP to 93 mmHg, which is still classified as hypertensive.157 Thus, practitioners would consider these results to be not clinically meaningful. In other words, these results are not useful in treating patients with hypertension. On the other hand, a small difference in effect that is statistically different may be of clinical importance, depending on the perspective of the reader. A long-term care pharmacist who specializes in geriatrics may consider a trial reporting a reduced number of incontinence episodes, on average, by two in a 24-hour period with a new anticholinergic

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agent compared to placebo to be clinically meaningful compared to an infectious disease pharmacist. The new drug may reduce nursing time and improve the overall QOL in patients with incontinence. Not all clinical trials reporting nonstatistically significant results are completely devoid of clinical importance. The overall effect of the intervention and control needs to be assessed. For example, a study compared lansoprazole (30 mg; n = 421) to omeprazole (20 mg; n = 431); each group received once daily therapy for a duration of 8 weeks. The healing rates of erosive reflux esophagitis was 87.2% versus 87%, respectively, via ITT analysis (p = nonsignificant [NS]).158 No clinical difference is concluded from this study, but these results would be considered to be clinically meaningful (i.e., clinically relevant) since >85% of patients were healed with either therapy. As previously mentioned, clinical trial results of the intervention may be statistically significant and clinically different than the control, but may be not clinically practical. The clinical trial methods need to be reviewed for the ability to replicate these into everyday patient care. A clinical trial may be designed that consists of technologies and/or include personnel that may not be readily accessible in patient care areas. In addition, patients may not be able to afford the new intervention. Another issue to consider is the demands on the actual patient. At times investigators offer incentives (e.g., monetary compensation, free medical care) for the subjects to strictly follow the study protocol (i.e., more motivated to be compliant). But in practice, real patients may not be as eager to follow an intricate schedule. For example, bismuth subsalicylate can be taken as a prophylaxis against traveler’s diarrhea.159 Although a clinical trial reported the suspension of subsalicylate bismuth 60 mL four times daily reduced the incidence of this unfortunate experience during travel,160 some individuals may not be willing to adhere to this dosing schedule. Another issue to consider before applying the clinical trial results to practice is the normal care for patients with the disease/condition under study. Endpoint results of an intervention may be statistically significant and clinically different than the active control, but the control is not normally prescribed for patients with the disease/condition.80

BIBLIOGRAPHY References are a very important part of the manuscript. The reference or bibliography section is at the end of the manuscript and provides documentation to support the information provided in the manuscript or acknowledgment for the work of other authors.44 Any material that the author uses in the manuscript should be appropriately cited. References included in the manuscript should be recent (e.g., outdated articles should not be used unless the results of the article are pertinent to the manuscript) and complete. Readers should scan the references listed in the bibliography to determine if the authors used material from reputable sources. In addition, authors should refrain from extensively

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citing only their own work.161 References typically should be listed in numerical order (e.g., Arabic numerals) in which these appear in the manuscript; however, several referencing styles exist and are journal dependent (refer to Appendix 9–3 for further information about referencing). At minimum, the information in the reference section should be sufficient to lead the reader to locating the same article. Some readers may wish to verify the cited information, while others search for articles in the reference section to gather additional information regarding a topic.44

ACKNOWLEDGMENTS Individuals contributing to the clinical trial, but who do not meet the requirements for authorship, can be recognized in this section (see Chapter 9 for more information). Examples of persons identified are those providing manuscript preparation, technical assistance, or donors of equipment or supplies. Medical writers or editors may also be listed if their contributions were significant. A collaboration or group may receive recognition in the acknowledgment section; however, many journals have a prespecified amount of space for the acknowledgment section that must be adhered to by authors. Authors must obtain written permission from persons acknowledged before listing in this section, so the readers do not infer endorsements of the data and conclusions from these contributors.34,44 Other types of information may be included in this section: financial support (see below for further information) and an indication that the manuscript underwent peer review, signified by a series of dates title received/revised/accepted. Typically, at least 4 to 8 weeks are between these dates since it is necessary to allow time for the reviewers to comment, the authors to revise, and then for another review of the manuscript. Some journals present only the manuscript acceptance date, which allows readers to determine the lag time between the article being accepted in final form to publication. Hopefully, a minimal time period exists between acceptance and the publication date, which increases content currency.

FUNDING 13 Controlled clinical trial investigators and authors should disclose any funding sources and

potential conflicts of interest. Due to the enormous expense often required to conduct a clinical trial, investigators may seek financial assistance to conduct the research. Various funding sources are available that include pharmaceutical companies, government agencies (e.g., National Institutes of Health [NIH]), national organizations (e.g., American Heart Association), university grants (e.g., faculty development grants), and private donations. Although financial disclosure is recommended, this may not occur. Reporting of payments by pharmaceutical companies to physicians who received greater than $100,000 has been assessed. The payments to the individuals were determined via the dollars for

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Docs database during 2009 to 2010. Approximately 69% of the 103 publications evaluated did not contain disclosure of payment that was shown in the dollars for Doc database.162 During 2012, an estimated $48.5 billion was spent by the pharmaceutical companies for research and development (which includes clinical drug trials), but interestingly this amount has been decreasing every year since 2010.163 The pharmaceutical industry is responsible for a significant amount of the clinical research conducted worldwide and new medications have resulted in a reduction in morbidity and mortality plus improved QOL for various disease states. Thus, readers of industry-sponsored research should not automatically disregard a clinical trial solely based on a pharmaceutical company sponsoring the research. However, readers should be cognizant of possible conflicts of interest, defined as “a set of conditions in which professional judgment concerning a primary interest (such as a patient’s welfare of the validity of research) tends to be unduly influenced by a secondary interest (such as financial gain)”164 that may result in potential bias. Conflicts of interest arise because the industry may be prompted to publish articles as a means of making their product appear better for a disease state in relation to the standard of care. This research may result in methods bias, premature termination of trials for nonscientific/unethical reasons, or reporting/publication bias.165 The ICMJE has adopted a disclosure form for all their member journals to obtain financial associations of the authors submitting manuscripts. The form is posted on the ICMJE Web site (http://www. icmje.org/coi_disclosure.pdf).166 The study design, result presentation, data interpretations, and study conclusions should be assessed appropriately to determine if the funding source had any influences on the overall clinical trial. Pharmaceutical companies need to determine the clinical usefulness of newly developed medications. These companies are expecting to profit from the new medication being approved by the FDA and marketed to prescribers. Many organizations (e.g., government, not for profit) are not prime candidates to offer funding for these studies, which leaves the pharmaceutical company to sponsor the study. Thus, readers are going to encounter pharmaceutical companies sponsoring research and the reader is responsible to determine the quality of the research. Not all investigator–pharmaceutical industry relationships have the potential to cause a conflict of interest, but readers should decide if a publication is biased. In fact, many well-designed, clinically important studies documenting improved patient health have been sponsored by the pharmaceutical industry and these have changed the standards of practice in treating patients. However, there have been reports in the literature of selected pharmaceutical companies: terminating studies for various reasons unrelated to efficacy and safety,167 employing inappropriate comparators,168 using inappropriate study samples,169 and suppressing the results of negative studies.170 Also, it can be assumed that a pharmaceutical company would design studies that are most likely to show superiority of their drug in some aspect

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or another. Furthermore, reports have been published that indicate a favorable conclusion of studies financially supported by the pharmaceutical industry, which can be referred to as publication bias. This type of sponsored research usually yields larger treatment effects than not-for-profit-funded studies.80 Research has documented that the conclusions of some trials funded by for-profit organizations were significantly in favor of the experimental drug as the treatment of choice. But not all pharmaceutical industry–sponsored research is biased; many study results are clinically meaningful. Readers need to be aware that the pharmaceutical company has a lot at stake for an investigational drug to be approved by the FDA. In response, the pharmaceutical company attempts to design a clinical trial to meet the FDA-approval standards. However, the methods of presenting (i.e., results section), interpreting (i.e., introduction and/or discussion sections), and summarizing the data and results (i.e., conclusion) can be biased and are not governed by the regulations of the FDA. Consequently, readers need to evaluate the trial data critically to assess the appropriateness and validity of the reported conclusions based on the trial results.171 Furthermore, trial registration with an official governmental entity is another method for the reader to discern if publication bias exists in favor of a particular interventional therapy. Specifically, the national clinical trials registry (http://www.clinicaltrials.gov),172 which is hosted by the NIH, is a very useful resource for the reader of biomedical literature to determine if potentially negative study results are being excluded from discussion in clinical trials or promotional materials. The Food and Drug Administration Amendments Act of 2007 (also known as FDAAA 801) mandates registration and results reporting certain clinical trials utilizing certain drugs, biologics, and devices, regardless of study outcome.173 ClinicalTrials.gov indexes >148,900 federally and privately funded clinical trials conducted in the United States and more than 180 countries. Furthermore, this resource provides the user details about a trial’s purpose, guidelines for participation, study locations, and contact information for more details. The information contained within the national trial registry should be strictly viewed as supplementary to complement care from a health care professional. This Web site can be searched by a variety of functions, including investigational agent or disease state. With this in mind, the national clinical trials registry is also a very useful tool for the practicing pharmacist who may need to identify treatment options for a patient who cannot afford conventional intervention or who has a rare or terminal disease state and is seeking additional treatment options.172

COMMENTARIES/CLINICAL TRIAL CRITIQUES Every journal should provide its readership with the opportunity for correspondence to exchange ideas about a topic or relay new information about articles published in the journal.34,44 14 Editorials, letters to the editor, and commentary publications can assist in

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interpreting controlled clinical trial results. Commentaries can be essential in assisting readers in interpreting and/or critiquing articles published within the journals by providing strengths and limitations of the original research, an update to published information, or questions to the authors of the original research manuscript. Editorials, defined as, “a written expression of opinion that may reflect the official position of the publication”44 are short essays from the editor or other experts in a particular field that are written to convey additional opinions about an article, typically, in the same issue of a journal. Not all editorials reflect the ideas/thoughts of the journal because these are opinions of the editorial author. Although editorials may contain some bias, this literature should always be considered when evaluating a clinical trial by providing additional insight into the results and aid in the comprehension of the clinical application of the trial results. For instance, an editorial in response to the CONDOR trial54 was published in the same journal issue. The editorial authors discussed many issues ranging from the exclusion of subjects taking aspirin (a large subgroup of patients who may have OA and are at increased risk for bleeds) to the short duration of the study, and a surrogate marker of the larger composite endpoint was a significant contributor of difference between therapies. The editorial authors concluded that the novelty of the study results are a welcomed addition to existing data, however, tempered some of the investigators’ conclusion regarding severity of lower GI bleeding.174 Several issues should be considered during the preparation or evaluation of editorials. Quality editorials are original; those editorials with nonoriginal ideas need to include a clear justification of repeating these ideas. The editorial objective should be clearly presented and reflect a complete message. The content should be significant to merit publication, along with being appliciable to practice, accurate, and thorough. The editorial points should be timely with respect to the publication in which the author is responding. Finally, the editorial author should mention the facts clearly and the material should be applicable to the readership of the publication.175 Not all original research reports are accompanied by an editorial. Persons seeking an editorial associated with a clinical trial can use a few methods to locate the publication. First, the journal issue that contains the clinical trial lists the editorial title in the journal issue table of contents. Another way, not always present in all clinical trials, is a notation printed on the first page of the clinical trial referring the reader to another page (i.e., “For comment, see page…”; “Commentary, page …”). Readers not having access to the actual clinical trial or journal issue table of contents can locate the trial citation in PubMed® (using the Single Citation Matcher at http://www.pubmed.gov). Those clinical trials with an accompanying editorial will contain a notation of Comment in and an abbreviated journal citation (i.e., journal name, date, plus volume, issue, and page numbers). Another method is to search the clinical trial topic (i.e., via Medical Subject Heading [MeSH] term in PubMed®) and limit the search to the publication type of editorial.

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Some journals/Web sites are published for the primary purpose of providing editorials/commentaries addressing a clinical trial. These resources are known as secondary journals and are independent of the journals that directly publish the clinical trials.36,80,176 Secondary journals are publications that assist the busy practitioners in a few vital methods: keeping them current regarding important and relevant studies, plus presenting key study information in a concise format. Clinical trials are presented, usually in a structured abstract style, but not just copying and pasting the exact abstract prepared by the trial investigators. These prepared abstracts may present additional and/or more precise information. In addition, a commentary addressing the study strengths, limitations, and applications into practice is authored by a leading practitioner in the field of study. Readers should use these resources while critiquing the biomedical/pharmacy literature. Examples of secondary journal Web sites include http://www.theheart.org and http://www.medscape.com. Typically, these publications provide an overview of the study followed by a commentary. Medscape is particularly useful for pharmacists since pharmacy-specific topics are addressed in a section of this Web site. ACP Journal Club is an online and print resource (included in Annals of Internal Medicine journal issues) in which biomedical literature (i.e., original research, systematic reviews) is selected, based on predefined criteria, and summarized by an expert in the field in the form of structured abstracts followed by a commentary. More than 130 journals are reviewed and are selected due to their potential impact on clinical practice.177 Another example is Journal Watch, a print and online resource that is published at least monthly in print178 and daily on the Internet.179 Updated information for 13 specialty areas of medical practice obtained from over 250 medical journals and other vital medical news sources is provided by physicians along with a commentary to help clinicians determine the impact or the research results on their practice.179 Several specialty editions of Journal Watch are available including Journal Watch Dermatology, Journal Watch Emergency Medicine, Journal Watch Gastroenterology, and Journal Watch Infectious Diseases.178

Letters to the Editor Letters to the editor can provide valuable insight into original research and can include various types of contributions. These may be in the form of comments, addenda, or updates from previously published articles, alerts regarding potential problems in practice, observations/comments on trends in medication use, opinions on trends or controversies in therapy or research, or original research. Authors of letters to the editor must adhere to strict guidelines from the journals regarding the length, number of tables, and format of the publication.180 The primary content of letters to the editor is feedback from the journal readers regarding the published materials in the journal. Typically, these letters are published within 3 to 6 months of the original publication. The letters may disagree with the design, result interpretation, and/or conclusions of the publication. Also, the letters may ask for

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additional information that can be used to interpret/clarify, comprehend, and/or critique the information within the publication. Afterward, the authors of the original publication may provide a response to these published letters. The letters to the editor serve as another source of valuble information for those using and critiquing the biomedical literature.

Conclusion Pharmacists are characterized by having the skills, ability, and knowledge to problem solve, critically think, and formulate recommendations based on the literature. All pharmacists need the skills of efficiently locating, critically evaluating, plus effectively formulating and communicating an evidence-based recommendation, regardless of the practice setting. As the role of the pharmacist in direct patient care continues to increase, incorporating these skills in daily practice is essential. A multitude of literature is published every year and the quality varies significantly. Readers of the literature should not immediately accept the authors’ conclusions but assess the strengths and limitations of the source. The information within this chapter identifies and discusses many issues to consider while reading and analyzing controlled clinical trials. Although every clinical trial has limitations, those trials with appropriate design and well-presented results are still important to apply to clinical practice. Using the proper techniques in evaluating clinical trials can allow pharmacists to contribute as key stakeholders in an ever increasing interdiscipilinary health care arena.

Case Study 4–2 A controlled clinical trial assessed the weight lowering effect of a new medication (lorcaserin) with a unique mechanism of action (novel selective agonist of the serotonin 2C receptor). Males and females were randomized to double-blind treatment with either lorcaserin 10 mg twice daily (n = 1561) or placebo (n = 1541) for 52 weeks. All subjects received the same specific diet and exercise routine. Overweight persons (body mass index of 30 to 45 kg/m2) between the ages of 18 and 65 years were eligible to be enrolled in this trial. The primary endpoint was the proportion of subjects losing at least 5% of their body weight. Secondary endpoints included changes in mean body weight and cardiovascular effects (e.g., heart valve function). The investigators conducted a power analysis (>90%) to determine the appropriate sample size. Statistical tests were two sided with α-value of 5%. Study results were analyzed using intention-to-treat.

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At the beginning of the trial, both groups were similar in terms of mean age (44 years), gender (females, 81%), mean body weight (100 kg), and comorbid conditions. The lorcaserin group had fewer subjects discontinued the trial as compared to the placebo group (43% versus 48%). More subjects treated with lorcaserin achieved at least a 5% body weight loss than those treated with placebo (47% versus 25%; p < 0.001). In addition, the mean body weight loss was greater with lorcaserin than with placebo (−5.8 versus −2.9 kg; p 10% and ≤20%; and large—an effect size >20%. Examples of the process measure of care included the frequency of prescribing a specific therapy, providing patient education, or test ordering that was in accordance with the guideline. Overall, 86% of interventions tested achieved positive improvements in process of care measures. There was considerable variation in the effect size of the interventions in different studies and in some studies between different interventions. The majority of interventions produced modest to moderate improvements in care. The lack of consistency of the differences between and within interventions did not permit any conclusion regarding the most effective strategy for guideline implementation. Multifaceted interventions were not found to be consistently more effective than single intervention strategies, and the number of components in the multifaceted interventions did not appear to be associated with effect size. The authors of this systematic review also noted that the overall quality of the methodology and reporting of the included studies were poor.42 This systematic review concluded that further research is required to develop and validate systems for estimation of the efficacy and efficiency of different strategies to implement patient, health professional, and organizational behavior change. Decision makers will have to evaluate the choice for implementation strategies carefully. Local factors, potential facilitators, and barriers to implementation are recommended for prominent consideration in this decision.42 As noted above, barriers to guideline implementation should be considered when making plans for this effort. Cabana and colleagues conducted a systematic review of the literature regarding barriers to physician adherence to clinical practice guidelines.43 A barrier was defined as any factor that limits or restricts complete physician adherence to a guideline. The authors identified seven general categories of barriers and provided examples or a description of each (Table 7–4). The relative importance of different barriers will vary depending on the characteristics of the specific guideline and on many local health care system characteristics.43 Paying appropriate attention to

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TABLE 74. SEVEN CATEGORIES OF BARRIERS43 Barrier Category

Examples of Barriers Identified or Description of Barrier

Lack of awareness Lack of familiarity

Did not know the guideline existed Could not correctly answer questions about guideline content or self-reported lack of familiarity Difference in interpretation of the evidence Benefits not worth patient risk, discomfort, or cost Not applicable to patient population in their practice Credibility of authors questioned Oversimplified cookbook Reduces autonomy Decreases flexibility Decreases physician self-respect Not practical Makes patient–physician relationship impersonal Did not believe that they could actually perform the behavior or activity recommended by the guideline, e.g., nutrition or exercise counseling Did not believe intended outcome would occur even if the practice was followed, e.g., counseling to stop smoking This barrier relates primarily to motivation to change practice, whether the motivation is professional, personal, or social. It was also noted that guidelines that recommend eliminating a behavior are more difficult to implement than guidelines that recommend adding a new behavior Patient resistance/nonadherence Patient does not perceive need Perceived to be offensive to patient Causes patient embarrassment Lack of reminder system Not easy to use, inconvenient, cumbersome, confusing Lack of educational materials Cost to patient Insufficient staff, consultant support or other resources Lack of time Lack of reimbursement Increased malpractice liability Not compatible with practice setting

Lack of agreement

Lack of self-efficacy Lack of outcome expectancy Inertia of previous practice

External barriers

these potential barriers in the planning and development of guidelines will facilitate successful implementation. An observational study of general practice in the Netherlands identified the following characteristics that influenced the use of guidelines: (1) specific attributes of the guidelines determine whether they are used in practice, (2) evidence-based recommendations

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are better followed in practice than those not based on scientific evidence, (3) precise definitions of recommended performance improve use, (4) testing the feasibility and acceptance of clinical guidelines among target groups is important, and (5) the people setting the guidelines need to understand the attributes of effective evidence-based guidelines.44 A computerized clinical decision support system (CDSS) is an information system designed to improve clinical decision making45 and is one method thought to facilitate guideline implementation. Garg and colleagues conducted a systematic review of controlled trials assessing the effects of CDSSs.45 Of the 100 trials they reviewed, 88% were randomized; 49% of these were cluster randomized; and 40% used a cluster as the unit of analysis or adjusted for clustering. The methodological quality of the trials was noted to improve over time. Twenty-nine studies involved drug dosing or prescribing. Of the 24 studies involving systems for single-drug dosing, 15 (62%) demonstrated improved practitioner performance with guidelines, and two of the 18 studies assessing patient outcomes showed positive improvement. Of the five systems using computer order entry for multidrug prescribing, four improved practitioner performance, but none improved patient outcomes. There were 40 studies of systems for disease management of conditions such as diabetes, cardiovascular disease prevention, urinary incontinence, human immunodeficiency virus infection, and acute respiratory distress. Thirty-seven of these studies evaluated practitioner performance with 23 (62%) demonstrating improvement. Only five (18%) of the 27 disease management trials evaluating patient outcomes demonstrated improvement. Of 21 trials of reminder systems for preventive care, 16 (72%) found improvements in practitioner performance according to practice guidelines. Of the 10 trials that evaluated CDSSs for diagnostic systems, only 4 (40%) found improvements in practitioner performance. Of the five trials of diagnostic systems that evaluated patient outcomes, none found improvement.45 The authors also reported that improved practitioner performance was associated with CDSSs that automatically prompted the practitioner compared to systems that required the practitioner to initiate system use.45 Improved performance was noted in 73% of trials of automated systems compared to 47% of user initiated systems (p = 0.02). It was also interesting to note that the best predictor of success of CDSSs was a study in which the authors were also the developers of the system. Studies conducted by the developer of the system were more likely to find success (74%), compared to 28% when the authors were not the developers (p = 0.001).45 It is clear that as with other methods for implementation of guidelines and achieving performance or behavior change, further research is needed on the use of CDSSs to provide clear guidance on predictable success rates. Many individual factors will need to be considered in the decision making for implementation of these systems.46

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A randomized, controlled trial of CQI and academic detailing to implement clinical guidelines for the primary care of hypertension and depression produced mixed results.47 The authors concluded that both academic detailing and CQI interventions involve complex social interactions that produce varied implementation success across the different organizations. Grimshaw and Russell published one of the first systematic literature reviews and evaluations of the effect of practice guidelines.48 They conducted an extensive literature search and identified 59 studies considered to have appropriate methods to evaluate the effect of guidelines on either physician behavior or patient outcomes. All but four of the studies showed some benefit from the guidelines; however, the magnitude of the benefit and the patient care significance was not impressive in all cases. Clinical practice guidelines represent an early application of decision support systems to facilitate the provision of quality clinical care. When done well, clinical practice guidelines should contain all the necessary elements of routine care for most individuals with a specific condition. They should prompt consideration of what specific characteristics of an individual patient might warrant departures from the guideline. When effectively implemented, such systems save clinicians time. They should be assisted by computerized systems that, among other functions, can catalog past histories, check orders for medications against measures of hepatic and renal function, and schedule reminders for screening tests or preventive services. They should be part of the continuous improvement of systems of care. Guidelines will not be perfect at the outset; systems that use them must be constructed so that experience can be applied to improve the guidelines, just as the guidelines indicate where care delivery can be improved.16

Case Study 7–4 Referring back to Case 7–1, the practice guideline panel finally has a new practice guideline ready to be implemented. As you already know, the most effective methods for implementing guidelines have not been determined. You are also aware that barriers have been identified to guideline implementation that should be considered when developing the implementation plan. Since the most effective methods for implementing guidelines have not been determined, the successful implementation plan may very well include those methods that address these barriers. With this in mind, what are the seven categories of barriers to guideline implementation that have been identified to limit or restrict complete prescriber adherence to a practice guideline?

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Sources of Clinical Practice Guidelines 9 Complete clinical practice guidelines can be found on Web sites such as the National

Guideline Clearinghouse and in the peer-reviewed medical literature located by use of secondary databases. Systematic reviews that can be helpful in developing or assessing specific practice guidelines are available from organizations such as the Cochrane Library, in addition to other Web sites that collect and provide health care–related information designed to support EBM such as the Agency for Healthcare Research and Quality (AHRQ). There are several mechanisms to locate completed clinical practice guidelines or systematic reviews. The National Guideline Clearinghouse (NGC) (http://www .guideline.gov) is maintained by the AHRQ. The mission of the NGC is to provide an accessible mechanism for obtaining objectives and detailed information on clinical practice guidelines and to further their dissemination, implementation, and use. Components of the NGC include structured abstracts about the guideline and its development, a utility for comparing attributes of two or more guidelines in a side-by-side comparison, synthesis of guidelines covering similar topics, highlighting areas of similarity and difference, links to fulltext guidelines where available and/or ordering information for print copies, and annotated bibliographies on guideline development methodology implementation and use. The NGC has published criteria for guidelines to be considered for inclusion in the Clearinghouse (http://www.guideline.gov/about/ inclusion-criteria.aspx). There are also criteria published at that location that will be effective in June 2014. These criteria provide useful information for quality consideration for guidelines. There are links that also present very useful commentary for further understanding of what guidelines are, characteristics for guideline developers, how guidelines should be produced and formatted, and expectations for documentation of these details. The new criteria reflect the most recent recommendations from the IOM that have been described in this chapter. Many guidelines have been published in the peer-reviewed medical literature and can, therefore, be located in MEDLINE®. A variety of search techniques may be used, but the most efficient may be to search for practice guideline in the publication type field of the record, or use the MeSH term practice guideline in conjunction with other terms for the specific disease or therapy of interest. Additional publication types in the record that may be searched include the terms: consensus development conference, guideline, and metaanalysis. Systematic review articles are also useful in preparation of clinical practice guidelines. The key differences with systematic reviews compared to the narrative review articles are that the systematic review begins with a focused clinical question, involves a comprehensive search for evidence, uses criterion-based selection that are uniformly

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applied to include evidence in the review, performs rigorous critical appraisal of the studies chosen, and provides a quantitative summary of the evidence.49 Literature search strategies have been published for locating systematic reviews.50,51 The National Institutes of Health (NIH) consensus statements, the U.S. Preventative Services Task Force (USPSTF) guides to clinical preventive services and evidence syntheses, AHRQ evidence reports and summaries, comparative effectiveness reviews, technical reviews and summaries, publications and reports of the Surgeon General, and other resources are available from their respective Web sites and also collectively at the Health Services/Technology Assessment Texts (HSTAT) Web site (http://www .ncbi.nlm.nih.gov/books/NBK16710), which is published by the National Library of Medicine. The Guidelines International Network (G-I-N) (http://www.g-i-n.net) is an international nonprofit association of international organizations and individuals involved in clinical practice guidelines. The G-I-N Library provides access to the International Guideline Library, development and training resources, relevant literature, health topic collection, relevant links, and other tools. Some of the resources from this Web site require membership for access. Multiple professional organizations, academic centers, independent research centers, and government agencies are involved in development of clinical practice guideline activities. Updated information may be obtained by contacting these organizations directly and many have provided access to their guidelines on their Web sites. Finally, the Cochrane Collaboration has a list of databases offering online access to medical evidence (http://www.cochrane.org/about-us/evidence-based-health-care/webliography/ databases).

Conclusion Clinical practice guidelines have become a significant tool in health care with the focus on evidence-based practice. Guidelines have the potential to assist medical decision making and ultimately improve the quality of care, improve patient outcomes, and make more efficient use of resources. Significant advances have been made in the methodology to produce valid guidelines. Information technology and greater understanding of optimal methods for implementation of guidelines will maximize their effect to improve quality of care. Pharmacists’ active involvement in preparation and implementation of evidencebased clinical practice guidelines is vital. A thorough understanding of evidence-based methodology will prepare the pharmacist to participate in this process. A drug information

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trained pharmacist is an ideal person to help prepare and/or implement evidence-based clinical practice guidelines.

Self-Assessment Questions 1. Which of the following groups or types of organizations have been involved in development of clinical practice guidelines? a. Federal and state governments b. Professional societies and associations c. Managed care organizations d. Third-party payers e. All of the above 2. Which of the following is a common characteristic of practice guideline development and traditional drug information practice activities? a. Decision making and recommendations based on individual experience b. Assurance of cost savings c. Clear specific definition of clinical questions d. Lack of interdisciplinary participation e. All of the above 3. Clinical practice guidelines are important to getting research information into practice because: a. Well studied new treatments proven effective are substantially underutilized b. Interventions proven ineffective or harmful continue to be provided c. Research information is not readily available for implementation into practice d. a and b only e. b and c only 4. Recommendations for optimizing patient care that are developed by systematically reviewing the evidence and assessing the benefits and harms of health care interventions is the definition for: a. Strengths of recommendations b. Evidence-based medicine c. Conflicts of interest d. Clinical practice guidelines e. Systematic reviews

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5. Which of the following are included in the five core competencies for health professionals as recommended in a landmark Institute of Medicine report? a. Deliver patient-centered care b. Participate in interdisciplinary teams c. Emphasize evidence-based practice d. Utilize informatics e. All of the above 6. The first step in the Institute of Medicine’s proposed standards for developing evidence-based clinical practice guidelines is: a. Conduct a systematic review for qualifying evidence. b. Establish a multidisciplinary guideline development group. c. Establish transparency. d. Manage conflict of interest. e. Conduct an external review. 7. Development of a clinical practice guideline should: a. Include a hospital administrator b. Be a multidisciplinary process c. Be made up of panel members without any conflicts of interest d. Have a pharmacist leading the effort e. All of the above 8. Which of the following characteristics associated with a disease would suggest that it would be a good topic for development and implementation of a practice guideline? a. Low prevalence b. Evidence that current practice is optimal c. Evidence of little variation in current practice d. Availability of high-quality evidence for the efficacy of treatments that reduce morbidity or mortality e. Low frequency and/or severity of morbidity or mortality 9. In the PICO model for framing clinical questions, the C represents: a. Collaboration b. Comparison c. Clinical d. Control e. Cohort 10. When using the GRADE system, which of the following information should be incorporated with guideline recommendations?

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a. b. c. d. e.

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Quality of evidence across studies for each important outcome Strength of the recommendations Balance between benefits and harms a and b only a, b, and c

11. When using the GRADE system, all of the following are reasons for lowering the level of confidence rating except: a. Risk of bias b. Publication bias c. Imprecision d. Indirectness e. Consistency 12. When using the GRADE system, which of the following is/are reason(s) for rating the level of confidence higher? a. Dose response b. Large effect c. All plausible confounding d. b and c only e. a, b, and c 13. All of the following are true regarding the AGREE II instrument for guideline evaluation except: a. Created by an international group of researchers and policy makers b. Provides a framework to assess the quality of guidelines c. Assesses the quality of the clinical content d. Intended for use by organizations and individuals e. Provides a methodological strategy for the development of guidelines 14. External barriers to clinical practice guideline implementation include all of the following except: a. Lack of familiarity with the guideline b. Cost to patient c. Increased malpractice liability d. Insufficient staff, consultant support, or other resources e. Lack of reimbursement 15. All of the following statements about clinical practice guidelines are true except: a. They should contain all the necessary elements of routine care for most individuals with a specific condition.

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b. They should not prompt consideration of what specific characteristics of an individual patient might warrant departures from the guideline. c. They represent an early application of decision support systems to facilitate providing quality clinical care. d. They will not be perfect at the outset. e. They should be part of the continuous improvement of systems of care.

REFERENCES 1. Institute of Medicine, Committee on Standards for Developing Trustworthy Clinical Practice Guidelines. Clinical practice guidelines we can trust. Graham R, Mancher M, Wolman DM, Greenfield S, Steinberg E, eds. Washington, DC: National Academies Press; 2011. Available from: http://www.iom.edu/Reports/2011/Clinical-Practice-GuidelinesWe-Can-Trust.aspx 2. Jones RH, Ritchie JL, Fleming BB, Hammermeister KE, Leape LL. 28th Bethesda Conference. Task Force 1: Clinical practice guideline development, dissemination and computerization. J Am Coll Cardiol. 1997;29:1133-41. 3. President’s Advisory Commission on Consumer Protection and Quality in the Health Care Industry. Quality first: better health care for all Americans. [cited: 2012 Dec 14]. Available from: http://archive.ahrq.gov/hcqual/final/. 4. Institute of Medicine, Committee on Quality Health Care in America. Crossing the quality chasm: a new health system for the 21st century. Washington, DC: National Academy Press; 2001. Available from: http://www.iom.edu/Reports/2001/Crossing-the-QualityChasm-A-New-Health-System-for-the-21st-Century.aspx 5. Institute of Medicine, Committee on Data Standards for Patient Safety. Patient safety: achieving a new standard for care. Aspden P, Corrigan JM, Wolcott J, Erikson SM, eds. Washington, DC: National Academies Press; 2004. Available from: http://www.iom.edu/ Reports/2003/Patient-Safety-Achieving-a-New-Standard-for-Care.aspx 6. Institute of Medicine, Committee on Quality of Health Care in America. To err is human: building a safer health system. Kohn LT, Corrigan JM, Donaldson MS, eds. Washington, DC: National Academy Press; 2000. Available from: http://iom.edu/Reports/1999/ToErr-is-Human-Building-A-Safer-Health-System.aspx 7. Institute of Medicine, Committee on Identifying and Preventing Medication Errors. Preventing medication errors. Aspden P, Wolcott J, Bootman JL, Cronenwett LR, eds. Washington, DC: National Academies Press; 2007. Available from: http://iom.edu/ Reports/2006/Preventing-Medication-Errors-Quality-Chasm-Series.aspx 8. Institute of Medicine, Committee on Standards for Systematic Reviews of Comparative Effectiveness Research. Finding what works in health care: standards for systematic reviews. Eden J, Levit L, Berg A, Morton S, eds. Washington, DC: National Academies Press; 2011. Available from: http://www.iom.edu/Reports/2011/Finding-What-Works-inHealth-Care-Standards-for-Systematic-Reviews.aspx

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9. Institute of Medicine, Committee on the Learning Health Care System in America. Best care at lower cost: the path to continuously learning health care in America. Smith M, Saunders R, Stuckhardt L, McGinnis JM, eds. Washington, DC: National Academies Press; 2012. Available from: http://www.iom.edu/Reports/2012/Best-Care-at-LowerCost-The-Path-to-Continuously-Learning-Health-Care-in-America.aspx 10. Sackett DL, Rosenberg WM, Gray JA, Haynes RB, Richardson WS. Evidence based medicine: what it is and what it isn’t. BMJ. 1996;312:71-2. 11. Eddy DM. Evidence-based medicine: a unified approach. Health Aff (Millwood). 2005;24:9-17. 12. Evidence-Based Medicine Working Group. Evidence-based medicine. A new approach to teaching the practice of medicine. JAMA. 1992;268:2420-5. 13. Straus SE, Glasziou P, Richardson WS, Haynes RB. Evidence-based medicine: how to practice & teach it. 4th ed. New York: Churchill Livingstone; 2011. 14. Watanabe AS, McCart G, Shimomura S, Kayser S. Systematic approach to drug information requests. Am J Hosp Pharm. 1975;32:1282-5. 15. Guyatt GH, Meade MO, Jaeschke RZ, Cook DJ, Haynes RB. Practitioners of evidence based care. Not all clinicians need to appraise evidence from scratch but all need some skills. BMJ. 2000;320:954-5. 16. Chassin MR. Is health care ready for Six Sigma quality? Milbank Q. 1998;76:510,565-91. 17. Fye WB. The power of clinical trials and guidelines, and the challenge of conflicts of interest. J Am Coll Cardiol. 2003;41:1237-42. 18. Choudhry NK, Stelfox HT, Detsky AS. Relationships between authors of clinical practice guidelines and the pharmaceutical industry. JAMA. 2002;287:612-7. 19. Curtiss FR. Consensus panel, national guidelines, and other potentially misleading terms. J Manag Care Pharm. 2003;9:574-5. 20. Van der Weyden MB. Clinical practice guidelines: time to move the debate from the how to the who. Med J Aust. 2002;176:304-5. 21. Guyatt GH, Akl EA, Crowther M, Schunemann HJ, Gutterman DD, Zelman LS. Introduction to the ninth edition: antithrombotic therapy and prevention of thrombosis, 9th ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2012;141:48S-52S. 22. Guyatt GH, Norris SL, Schulman S, Hirsh J, Eckman MH, Akl EA, et al. Methodology for the development of antithrombotic therapy and prevention of thrombosis guidelines: antithrombotic therapy and prevention of thrombosis, 9th ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2012;141:53S-70S. 23. Mosca L, Appel LJ, Benjamin EJ, Berra K, Chandra-Strobos N, Fabunmi RP, et al. Evidence-based guidelines for cardiovascular disease prevention in women. J Am Coll Cardiol. 2004;43:900-21. 24. Guyatt GH, Oxman AD, Schunemann HJ, Tugwell P, Knottnerus A. GRADE guidelines: a new series of articles in the Journal of Clinical Epidemiology. J Clin Epidemiol. 2011;64:380-2.

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25. Atkins D, Best D, Briss PA, Eccles M, Falck-Ytter Y, Flottorp S, et al. Grading quality of evidence and strength of recommendations. BMJ. 2004;328:1490. 26. Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alfonso-Coello P, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008;336:924-6. 27. Guyatt GH, Oxman AD, Kunz R, Atkins D, Brozek J, Vist G, et al. GRADE guidelines: 2. Framing the question and deciding on important outcomes. J Clin Epidemiol. 2011;64:395400. 28. Balshem H, Helfand M, Schunemann HJ, Oxman AD, Kunz R, Brozek J, et al. GRADE guidelines: 3. Rating the quality of evidence. J Clin Epidemiol. 2011;64:401-6. 29. Guyatt GH, Oxman AD, Vist G, Kunz R, Brozek J, Alfonso-Coello P, et al. GRADE guidelines: 4. Rating the quality of evidence-study limitations (risk of bias). J Clin Epidemiol. 2011;64:407-15. 30. Guyatt GH, Oxman AD, Kunz R, Woodcock J, Brozek J, Helfand M, et al. GRADE guidelines: 7. Rating the quality of evidence—inconsistency. J Clin Epidemiol. 2011;64:1294302. 31. Guyatt GH, Oxman AD, Kunz R, Woodcock J, Brozek J, Helfand M, et al. GRADE guidelines: 8. Rating the quality of evidence-indirectness. J Clin Epidemiol. 2011;64:1303-10. 32. Guyatt GH, Oxman AD, Kunz R, Brozek J, Alfonso-Coello P, Rind D, et al. GRADE guidelines 6. Rating the quality of evidence-imprecision. J Clin Epidemiol. 2011;64:1283-93. 33. Guyatt GH, Oxman AD, Montori V, Vist G, Kunz R, Brozek J, et al. GRADE guidelines: 5. Rating the quality of evidence-publication bias. J Clin Epidemiol. 2011;64:1277-82. 34. Guyatt GH, Oxman AD, Sultan S, Glasziou P, Akl EA, Alfonso-Coello P, et al. GRADE guidelines: 9. Rating up the quality of evidence. J Clin Epidemiol. 2011;64:1311-6. 35. Hill AB. The environment and disease: association or causation? Proc R Soc Med. 1965;58:295-300. 36. Guyatt G, Oxman AD, Akl EA, Kunz R, Vist G, Brozek J, et al. GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables. J Clin Epidemiol. 2011;64:383-94. 37. Guyatt GH, Oxman AD, Santesso N, Helfand M, Vist G, Kunz R, et al. GRADE guidelines: 12. Preparing summary of findings tables-binary outcomes. J Clin Epidemiol. 2013;66:15872. 38. Guyatt GH, Thorlund K, Oxman AD, Walter SD, Patrick D, Furukawa TA, et al. GRADE guidelines: 13. Preparing summary of findings tables and evidence profiles-continuous outcomes. J Clin Epidemiol. 2013;66:173-83. 39. Brouwers MC, Kho ME, Browman GP, Burgers JS, Cluzeau F, Feder G, et al. AGREE II: advancing guideline development, reporting and evaluation in health care. CMAJ. 2010;182:E839-42. 40. AGREE Next Steps Consortium. Appraisal of Guidelines for Research & Evaluation (AGREE) II Instrument. [cited: 2012 Dec 14]. Available from: http://www.agreetrust

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41.

42.

43.

44.

45.

46. 47.

48. 49. 50. 51.

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.org/wp-content/uploads/2013/10/AGREE-II-Users-Manual-and-23-item-Instrument_2009_ UPDATE_2013.pdf Davis DA, Taylor-Vaisey A. Translating guidelines into practice. A systematic review of theoretic concepts, practical experience and research evidence in the adoption of clinical practice guidelines. CMAJ. 1997;157:408-16. Grimshaw JM, Thomas RE, MacLennan G, Fraser C, Ramsay CR, Vale L, et al. Effectiveness and efficiency of guideline dissemination and implementation strategies. Health Technol Assess. 2004;8:iii-72. Cabana MD, Rand CS, Powe NR, Wu AW, Wilson MH, Abboud PA, et al. Why don’t physicians follow clinical practice guidelines? A framework for improvement. JAMA. 1999;282: 1458-65. Grol R, Dalhuijsen J, Thomas S, Veld C, Rutten G, Mokkink H. Attributes of clinical guidelines that influence use of guidelines in general practice: observational study. BMJ. 1998;317:858-61. Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005;293:1223-38. Wears RL, Berg M. Computer technology and clinical work: still waiting for Godot. JAMA. 2005;293:1261-3. Horowitz CR, Goldberg HI, Martin DP, Wagner EH, Fihn SD, Christensen DB, et al. Conducting a randomized controlled trial of CQI and academic detailing to implement clinical guidelines. Jt Comm J Qual Improv. 1996;22:734-50. Grimshaw JM, Russell IT. Effect of clinical guidelines on medical practice: a systematic review of rigorous evaluations. Lancet. 1993;342:1317-22. Cook DJ, Mulrow CD, Haynes RB. Systematic reviews: synthesis of best evidence for clinical decisions. Ann Intern Med. 1997;126:376-80. Hunt DL, McKibbon KA. Locating and appraising systematic reviews. Ann Intern Med. 1997;126:532-8. Montori VM, Wilczynski NL, Morgan D, Haynes RB. Optimal search strategies for retrieving systematic reviews from Medline: analytical survey. BMJ. 2005;330:68.

SUGGESTED READINGS 1. AGREE Next Steps Consortium. Appraisal of Guidelines for Research & Evaluation (AGREE) II Instrument. Available from: http://www.agreetrust.org/wp-content/ uploads/2013/10/AGREE-II-Users-Manual-and-23-item-Instr ument_2009_ UPDATE_2013.pdf 2. Guyatt GH, Oxman AD, Schunemann HJ, Tugwell P, Knottnerus A. GRADE guidelines: a new series of articles in the Journal of Clinical Epidemiology. J Clin Epidemiol. 2011;64:380-2.

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3. Institute of Medicine Committee on Standards for Developing Trustworthy Clinical Practice Guidelines. Clinical practice guidelines we can trust. In: Graham R, Mancher M, Wolman DM, Greenfield S, Steinberg E, eds. Washington, D.C.: National Academies Press; 2011. Available from: http://www.iom.edu/Reports/2011/Clinical-Practice-GuidelinesWe-Can-Trust.aspx 4. Institute of Medicine Committee on Standards for Systematic Reviews of Comparative Effectiveness Research. Finding what works in health care: standards for systematic reviews. In: Eden J, Levit L, Berg A, Morton S, eds. Washington, D.C.: National Academies Press; 2011. Available from: http://www.iom.edu/Reports/2011/Finding-WhatWorks-in-Health-Care-Standards-for-Systematic-Reviews.aspx

8

C hapter Eight

The Application of Statistical Analysis in the Biomedical Sciences Ryan W. Walters

Learning Objectives After completing this chapter, the reader will be able to •

•

• • •

•

• •

•

Define the population being studied and describe the method most appropriate to sample a given population. Identify and describe the dependent and independent variables and indicate whether any covariates were included in analysis. Identify and define the four scales of variable measurement. Describe the difference between descriptive and inferential statistics. Describe the mean, median, variance, and standard deviation and why they are important to statistical analysis. Describe the properties of the normal distribution and when an alternative distribution should be, or should have been, used. Describe several common epidemiological statistics. Identify and describe the difference between parametric and nonparametric statistical tests and when their use is most appropriate. Determine whether the appropriate statistical test has been performed when evaluating a study.

Key Concepts 1 There are four scales of variable measurement consisting of nominal, ordinal, interval,

and ratio scales that are critically important to consider when determining the appropriateness of a statistical test. 351

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2 Measures of central tendency are useful to quantify the distribution of a variable’s data

3 4

5 6

7 8 9

numerically. The most common measures of central tendency are the mean, median, and mode, with the most appropriate measure of central tendency dictated by the variable’s scale of measurement. Variance is a key element inherent in all statistical analyses, but standard deviation is presented more often. Variance and standard deviation are related mathematically. The key benefit to using the standard normal distribution is that converting the original data to z-scores allows researchers to compare different variables regardless of the original scale. The last observation carried forward (LOCF) technique used often with the data from clinical trials introduces significant bias into the results of statistical tests. The central limit theorem states when equally sized samples are drawn from a nonnormal distribution, the plotted mean values from each sample will approximate a normal distribution as long as the non-normality was not due to outliers. There are numerous misconceptions about p values and it is important to know how to interpret them correctly. Clinical significance is far more important than statistical significance. Clinical significance can be quantified by using various measures of effect size. The selection of the appropriate statistical test is based on several factors including the specific research question, the measurement scale of the dependent variable (DV), distributional assumptions, the number of DV measurements as well as the number and measurement scale of independent variables (IVs) and covariates, among others.

Introduction Knowledge of statistics and statistical analyses is essential to constructively evaluate literature in the biomedical sciences. This chapter provides a general overview of both descriptive and inferential statistics that will enhance the ability of the student or evidence-based practitioner to interpret results of empirical literature within the biomedical sciences by evaluating the appropriateness of statistical tests employed, the conclusions drawn by the authors, and the overall quality of the study. Alongside Chapters 4 and 5, diligent study of the material presented in this chapter is an important first step to critically analyze the often avoided methods or results sections of published biomedical literature. Be aware, however, that this chapter cannot substitute for more formal didactic training in statistics, as the material presented here is not exhaustive with regards to either statistical concepts or available statistical tests. Thus, when reading a journal article, if doubt emerges about whether a method or statistical test was

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used and interpreted appropriately, do not hesitate to consult appropriate references or an individual who has more formal statistical training. This is especially true if the empirical evidence is being considered for implementation in practice. Asking questions is the key to obtaining knowledge! For didactic purposes, this chapter can be divided into two sections. The first section presents a general overview of the processes underlying most statistical tests used in the biomedical sciences. It is recommended that all readers take the time required to thoroughly study these concepts. The second section, beginning with the Statistical Tests section, presents descriptions, assumptions, examples, and results of numerous statistical tests commonly used in the biomedical sciences. This section does not present the mathematical underpinnings, calculation, or programming of any specific statistical test. It is recommended that this section serve as a reference to be used concurrently alongside a given journal article to determine the appropriateness of a statistical test or to gain further insight into why a specific statistical test was employed.

Populations and Sampling When investigating a particular research question or hypothesis, researchers must first define the population to be studied. A population refers to any set of objects in the universe, while a sample is a fraction of the population chosen to be representative of the specific population of interest. Thus, samples are chosen to make specific generalizations about the population of interest. Researchers typically do not attempt to study the entire population because data cannot be collected for everyone within a population. This is why a sample should ideally be chosen at random. That is, each member of the population must have an equal probability of being included in the sample. For example, consider a study to evaluate the effect a calcium channel blocker (CCB) has on blood glucose levels in Type 1 diabetes mellitus (DM) patients. In this case, all Type 1 DM patients would constitute the study population; however, because data could never be collected from all Type 1 DM patients, a sample that is representative of the Type 1 DM population would be selected. There are numerous sampling strategies, many beyond the scope of this text. Although only a few are discussed here, interested readers are urged to consult the list of suggested readings at the end of this chapter for further information. A random sample does not imply that the sample is drawn haphazardly or in an unplanned fashion, and there are several approaches to selecting a random sample. The most common method employs a random number table. A random number table theoretically contains all integers between one and infinity that have been selected without any

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trends or patterns. For example, consider the hypothetical process of selecting a random sample of Type 1 DM patients from the population. First, each patient in the population is assigned a number, say 1 to N, where N is the total number of Type 1 DM patients in the population. From this population, a sample of 200 patients is requested. The random number table would randomly select 200 patients from the population of size N. There are numerous free random number tables and generators available online; simply search for random number table or random number generator in any search engine. Depending on the study design, a random sample may not be most appropriate when selecting a representative sample. On occasion, it may be necessary to separate the population into mutually exclusive groups called strata, where a specific factor (e.g., race, gender) will create separate strata to aid in analysis. In this case, the random sample is drawn within each stratum individually. This process is termed stratified random sampling. For example, consider a situation where the race of the patient was an important variable in the Type 1 DM study. To ensure the proportion of each race in the population is represented accurately, the researcher stratifies by race and randomly selects patients within each stratum to achieve a representative study sample. Another method of randomly sampling a population is known as cluster sampling. Cluster sampling is appropriate when there are natural groupings within the population of interest. For example, consider a researcher interested in the patient counseling practices of pharmacists across the United States. It would be impossible to collect data from all pharmacists across the United States. However, the researcher has read literature suggesting regional differences within various pharmacy practices, not necessarily including counseling practices. Thus, he or she may decide to randomly sample within the four regions of the United States (U.S.) defined by the U.S. Census Bureau (i.e., West, Midwest, South, and Northeast) to assess for differences in patient counseling practices across regions.1 Another sampling method is known as systematic sampling. This method is used when information about the population is provided in list format, such as in the telephone book, election records, class lists, or licensure records, among others. Systematic sampling uses an equal-probability method where one individual is selected initially at random and every nth individual is then selected thereafter. For example, the researchers may decide to take every 10th individual listed after the first individual is chosen. Finally, researchers often use convenience sampling to select participants based on the convenience of the researcher. That is, no attempt is made to ensure the sample is representative of the population. However, within the convenience sample, participants may be selected randomly. This type of sampling is often used in educational research. For example, consider a researcher evaluating a new classroom instructional method to increase exam scores. This type of study will use the convenience sample of the students in their own class or university. Obviously, significant weaknesses are apparent when using this type of sampling, most notably, limited generalization.

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Variables and the Measurement of Data A variable is the characteristic that is being observed or measured. Data are the measured values assigned to the variable for each individual member of the population. For example, a variable would be the participant’s biological sex, while the data is whether the participant is male or female. In statistics, there are three types of variables: dependent (DV), independent (IV), and confounding. The DV is the response or outcome variable for a study, while an IV is a variable that is manipulated. A confounding variable (often referred to as covariate) is any variable that has an effect on the DV over and above the effect of the IV, but is not of specific research interest. Putting these definitions together, consider a study to evaluate the effect a new oral hypoglycemic medication has on glycosylated hemoglobin (HbA1c) compared to placebo. Here, the DV consists of the HbA1c data for each participant, and the IV is treatment group with two levels (i.e., treatment versus placebo). Initially, results may suggest the medication is very effective across the entire sample; however, previous literature has suggested participant race may affect the effectiveness of this type of medication. Thus, participant race is a confounding variable and would need to be included in the statistical analysis. After statistically controlling for participant race, the results may indicate the medication was significantly more effective in the treatment group.

SCALES OF MEASUREMENT 1 There are four scales of variable measurement consisting of nominal, ordinal, interval,

and ratio scales that are critically important to consider when determining the appropriateness of a statistical test.2 Think of these four scales as relatively fluid; that is, as the data progress from nominal to ratio, the information about each variable being measured is increased. The scale of measurement of DVs, IVs, and confounding variables is an important consideration when determining whether the appropriate statistical test was used to answer the research question and hypothesis. A nominal scale consists of categories that have no implied rank or order. Examples of nominal variables include gender (e.g., male versus female), race (e.g., Caucasian versus African American versus Hispanic), or disease state (e.g., absence versus presence). It is important to note that with nominal data, the participant is categorized into one, and only one, category. That is, the categories are mutually exclusive. An ordinal scale has all of the characteristics of a nominal variable with the addition of rank ordering. It is important to note the distance between rank ordered categories cannot be considered equal; that is, the data points can be ranked but the distance between them may differ greatly. For example, in medicine a commonly used pain scale is the Wong-Baker Faces Pain Rating Scale.3 Here, the participant ranks pain on a 0 to 10 scale; however, while

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it is known that a rating of eight indicates the participant is in more pain than rating four, there is no indication a rating of eight hurts twice as much as a rating of four. An interval scale has all of the characteristics of an ordinal scale with the addition that the distance between two values is now constant and meaningful. However, it is important to note that interval scales do not have an absolute zero point. For example, temperature on a Celsius scale is measured on an interval scale (i.e., the difference between 10°C and 5°C is equivalent to the difference between 20°C and 15°C). However, 20°C/10°C cannot be quantified as twice as hot because there is no absolute zero (i.e., the selection of 0°C was arbitrary). Finally, a ratio scale has all of the characteristics of an interval scale, but ratio scales have an absolute zero point. The classic example of a ratio scale is temperature measured on the Kelvin scale, where zero Kelvin represents the absence of molecular motion. Theoretically, researchers should not confuse absolute and arbitrary zero points. However, the difference between interval and ratio scales is generally trivial as these data are analyzed by identical statistical procedures.

CONTINUOUS VERSUS CATEGORICAL VARIABLES Continuous variables generally consist of data measured on interval or ratio scales. However, if the number of ordinal categories is large (e.g., seven or more) they may be considered continuous.4 Be aware that continuous variables may also be referred to as quantitative in the literature. Examples of continuous variables include age, body mass index (BMI), or uncategorized systolic or diastolic blood pressure values. Categorical variables consist of data measured on nominal or ordinal scales because these scales of measurement have naturally distinct categories. Examples of categorical variables include gender, race, or blood pressure status (e.g., hypotensive, normotensive, prehypertensive, hypertensive). In the literature, categorical variables are often termed discrete or if a variable is measured on a nominal scale with only two distinct categories it may be termed binary or dichotomous. Note that it is common in the biomedical literature for researchers to categorize continuous variables. While not wholly incorrect, categorizing a continuous variable will always result in loss of information about the variable. For example, consider the role participant age has on the probability of experiencing a cardiac event. Although age is a continuous variable, younger individuals typically have much a lower probability compared to older individuals. Thus, the research may divide age into four discrete categories: 3.0 IQRs.)

Finally, a scatterplot presents data for two variables both measured on a continuous scale. That is, the x-axis contains the range of data for one variable, whereas the y-axis contains the range of data for a second variable. In general, the axis choice for a given variable is arbitrary. Data are plotted in a similar fashion to plotting data on a coordinate plane during an introductory geometry class. Figure 8–4 presents a scatterplot of height in inches and body weight in pounds. The individual circles in the scatterplot are a participant’s height in relation to their weight. Because the plot is bivariate (i.e., there are two variables), participants must have data for both variables in order to be plotted. Scatterplots are useful in visually assessing the association between two variables as well as assessing assumptions of various statistical tests such as linearity and absence of outliers.

Common Probability Distributions Up to this point, data distributions have been discussed using very general terminology. There are numerous distributions available to researchers; far too many to provide a complete listing, but all that needs to be known about these available distributions is that each has different characteristics to fit the unique requirements of the data. Globally, these

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80 79 78 77 76

Height (in)

75 74 73 72 71 70 69 68 67 66 65 170

175

180

185 Body weight (lb)

190

195

200

Figure 8–4. Scatterplot of height by weight.

distributions are termed probability distributions, and they are incredibly important to every statistical analysis conducted. Thus, the choice of distribution used in a given statistical analysis is nontrivial as the use of an improper distribution can lead to incorrect statistical inference. Of the distributions available, the normal and binomial distributions are used most frequently in the biomedical literature; therefore, these are discussed in detail. To provide the reader with a broader listing of available distributions, this section also presents brief information about other common distributions used in the biomedical literature and when they are appropriately used in statistical analyses.

THE NORMAL DISTRIBUTION The normal distribution, also called Gaussian distribution, is the most commonly used distribution in statistics and one that occurs frequently in nature. It is used only for continuous variables measured on interval or ratio scales. The normal distribution has several easily identifiable properties based on the numerical measures of central tendency, variability, and shape. Specifically, this includes the following characteristics: 1. The primary shape is bell-shaped. 2. The mean, median, and mode are equal.

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3. The distribution has one mode, is symmetric, and reflects itself perfectly when folded at the mean. 4. The skewness and kurtosis are zero. 5. The area under a normal distribution is, by definition, equal to one. It should be noted that the five properties above are considered the gold standard. In practice, however, each of these properties will be approximated; namely, the curve will be roughly bell-shaped, the mean, median, and mode will be roughly equal, and skewness and kurtosis may be evident but not greatly exaggerated. For example, the distribution of data for Group 2 in Figure 8–1 is approximately normal. Several additional properties of the normal distribution are important; consider Figure 8–5. First, the distribution is completely defined by the mean and standard deviation. Consequently, there are an infinite number of possible normal distributions because there are an infinite number of mean and standard deviation combinations. In the literature, this property is often stated as the mean and standard deviation being sufficient statistics for describing the normal distribution. Second, the mean can always be identified as the peak (or mode) of the distribution. Third, the standard deviation will always dictate the spread of the distribution. That is, as the standard deviation increases, the distribution becomes wider. Finally, roughly 68% of the data will occur within one standard deviation above and below the mean, roughly 95% within two standard deviations, and roughly 99% within three standard deviations.

THE STANDARD NORMAL DISTRIBUTION Among the infinite number of potential normal distributions, only the standard normal distribution can be used to compare all normal distributions. Although this may seem

.3413 .0013

–4

.3413

.1359 –3

.0215

–2

.0013

.1359 –1

0

1

68.26% 94.44% 99.74%

Figure 8–5. The normal distribution.

2

.0215

3

4

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confusing on the surface, a clearer understanding of the standard normal distribution is made possible by considering the standard deviation. Initially, when converting a normal distribution to a standard normal distribution, the data must be converted into standardized scores referred to as z-scores. A z-score converts the units of the original data into standard deviation units. That is, a z-score indicates how many standard deviations a data point is from the mean. When converted to z-scores, the new standard normal distribution will always have a mean of zero and a standard deviation of one. The standard normal distribution is presented in Figure 8–5. It is a common misconception that converting data into z-scores creates a standard normal distribution from data that was not normally distributed. This is never true. A standardized distribution will always have the same characteristics of distribution from which it originated. That is, if the original distribution was skewed, the standardized distribution will also be skewed. 4 The key benefit to using the standard normal distribution is that converting the original data to z-scores allows researchers to compare different variables regardless of the original scale. Remember, a standardized variable will always be expressed in standard deviation units with a mean of zero and standard deviation of one. Therefore, differences between variables may be more easily detected and understood. For example, it is possible to compare standardized variables across studies. It should go without stating that the only requirement is that that both variables measure the same construct. After z-score standardization, the age of two groups of participants from two different studies can be compared directly.

THE BINOMIAL DISTRIBUTION Many discrete variables can be dichotomized into one of two mutually exclusive groups, outcomes, or events (e.g., dead versus alive). Using the binomial distribution, a researcher can calculate the exact probability of experiencing either binary outcome. The binomial distribution can only be used when an experiment assumes the four characteristics listed below: 1. The trial occurs a specified number of times (analogous to sample size, n). 2. Each trial has only two mutually exclusive outcomes (success versus failure in a generic sense, x). Also, be aware that a single trial with only two outcomes is known as a Bernoulli trial, a term that may be encountered in the literature. 3. Each trial is independent, meaning that one outcome has no effect on the other. 4. The probability of success remains constant throughout the trial. An example may assist with the understanding of these characteristics. The binomial distribution consists of the number of successes and failures during a given study period.

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When all trials have been run, the probability of achieving exactly x successes (or failures) in n trials can be calculated. Consider flipping a fair coin. The coin is flipped for a set number of trials (i.e., n), there are only two possible outcomes (i.e., heads or tails), each trial is not affected by the outcome of the last, and the probability of flipping heads or tails remains constant throughout the trial (i.e., 0.50). By definition, the mean for the binomial distribution is equal to the number of successes in a given trial. That is, if a fair coin is flipped 10 times and heads turns up on six flips, the mean is 0.60 (i.e., 6/10). Further, the variance of the binomial distribution is fixed by the mean. While the equation for the variance is not presented, know that the variance is largest at a mean of 0.50, decreases as the mean diverges from 0.50, and is symmetric (e.g., the variance for a mean of 0.10 is equal to the variance for a mean of 0.90). Therefore, because variance is fixed by the mean, the mean is the sufficient statistic for the binomial distribution. In reality, the probability of experiencing an outcome is rarely 0.50. For example, biomedical studies often use all-cause mortality as an outcome variable, and the probability of dying during the study period is generally lower than staying alive. At the end of the trial, participants can experience only one of the outcomes—dead or alive. Say a study sample consists of 1000 participants, of which 150 die. The binomial distribution allows for the calculation of the exact probability of having 150 participants die in a sample of 1000 participants.

OTHER COMMON PROBABILITY DISTRIBUTIONS As mentioned in the introduction to this section, there are numerous probability distributions available to researchers, with their use determined by the scale of measurement of the DV as well as the shape (e.g., skewness) of the distribution. When reading journal articles, it is important to know whether the appropriate distribution has been used for statistical analysis as inappropriate use of any distribution can lead to inaccurate statistical inference. Table 8–1 provides a short list of the several commonly used distributions in the biomedical literature. Of note here is that alternative distributions are available when statistically analyzing non-normally distributed continuous data or data that cannot be normalized such as categorical data. The take away message here is that non-normal continuous data does not need to be forced to conform to a normal distribution. The only DV scale presented in Table 8–1 that has not been discussed thus far is count data. An example of count data would be a count of the number of hospitalizations during a 5-year study period. It is clear from the example that count data cannot take on negative values. That is, a participant cannot have a negative number of hospitalizations. Both the Poisson and negative binomial distributions are used when analyzing count data.

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TABLE 81. COMMON PROBABILITY DISTRIBUTIONS AND WHEN THEY ARE APPROPRIATE TO USE Distribution Name

DV Scale

Normal Gamma Inverse Gaussian Exponential Log-normala Weibull Gompertz Poisson Negative binomial Bernoulli Binomial

Continuous Continuous Continuous Continuous Continuous Continuous Continuous Count Count Binary Binary

Categorical Multinomial

Categorical Categorical

Distribution Characteristics Positive skew Positive skew Positive skew Positive skew Positive or negative skew Positive or negative skew Mean = variance Mean ≠ variance

Comments

Data has to be > 0 Data has to be > 0

Data ≥ 0 Data ≥ 0 One trial; 2 categories Repeated Bernoulli trials; 2 categories One trial; > 2 categories Repeated trials; > 2 categories

Log = natural log or ln.

a

The Poisson distribution assumes the mean and the variance of the data are identical. However, in situations where the mean and variance are not equal, the negative binomial distribution allows the variance of the distribution to increase or decrease as needed. As a point of possible confusion, the negative binomial distribution does not allow negative values. The negative in negative binomial is a result of using a negative exponent in its mathematical formula.

TRANSFORMING NONNORMAL DISTRIBUTIONS Transformations are usually employed by researchers to transform a non-normal distribution into a distribution that is approximately normal. Although on the surface this may appear reasonable, data transformation is an archaic technique. As such, transformation is not recommended for the three reasons provided below. First, although parametric statistical analyses assume normality, the distribution of the actual DV data is not required to be normally distributed. As stated in the previous section, and highlighted in Table 8–1, if non-normality is observed, alternative distributions exist allowing proper statistical analysis of non-normal data without transformation. For this reason alone, transformation is rarely necessary. Second, transforming the DV data potentially prevents effects of an IV from being observed. As an overly simplistic example, consider the bimodal distribution of body weight data presented in Figure 8–6. Say the body weight data were collected from a

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50

Frequency

40

30

20

10

0

110 120 130 140 150 160 170 180 190 200 210 220 230 240 Body weight (lb)

Figure 8–6. Body weight data for a group of men and women.

sample of men and women. Obviously, the data in Figure 8–6 is bimodal and not normally distributed. In this situation, a researcher may attempt to transform the data, but transformation would obscure the effects of an obvious IV—gender. That is, women tend to be lighter compared to men, and this is observed in these data, with women being grouped in the left distribution and men grouped in the right distribution. If transformation were performed successfully, inherent differences between men and women would be erased. Third, while data transformation will not affect the rank order of the data, it will significantly cloud interpretation of statistical analyses. That is, after transformation all results must be interpreted in the transformed metric, which can become convoluted quickly making it difficult to apply results to the untransformed data used in the real world. Thus, when reading a journal article where the authors employed any transformation, be aware the results are in the transformed metric and ensure the authors’ interpretations remain consistent with this metric. Although transformation is not recommended, it can be correct, and it will inevitably be encountered when reading the literature, especially for skewed data or data with outliers. Therefore, it is useful to become familiar with several of the techniques used to transform data. Transformations for positive skewness differ from those suggested for negative skewness. Mild positive skewness is typically remedied by a square root transformation.

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Here, the square root of all data is calculated and this square root data is used in analysis. When positive skewness is severe, a natural log transformation is used. When data are skewed negatively, researchers may choose to reflect their data to make it positively skewed and apply the transformations for positive skewness described above. To reflect data, a 1 is added to the absolute value of the highest data point and all data points are subtracted from this new value. For example, if the highest value in the data is 10, a 1 is added to create 11, and then all data points are subtracted from 11 (e.g., 11 − 10 = 1, 11 − 1 = 10, etc.). In this manner, the highest values become the lowest and the lowest become the highest. It should be clear that this process considerably convolutes data interpretation!

Epidemiological Statistics The field of epidemiology investigates how diseases are distributed in the population and the various factors (or exposures) influencing this distribution.6 Epidemiological statistics are not unique to the field of epidemiology, as much of the literature in the biomedical sciences incorporates some form of these statistics such as odds ratios. Thus, it is important to have at least a basic level of understanding of these statistics. In this section, the most commonly used epidemiological statistics are discussed briefly including ratios, proportions, and rates, incidence and prevalence, relative risk and odds ratios as well as sensitivity, specificity, and predictive values.

RATIO, PROPORTIONS, AND RATES Ratios, proportions, and rates are terms used interchangeably in the medical literature without regard for the actual mathematical definitions. Further, there are a considerable number of proportions and rates available to researchers, each providing unique information. Thus, it is important to be aware of how each of these measures are defined and calculated.7 A ratio expresses the relationship between two numbers. For example, consider the ratio of men to women diagnosed with multiple sclerosis (MS). If, in a sample consisting of only MS patients, 100 men and 50 women are diagnosed, the ratio of men to women is 100 to 50, or 100:50, or 2:1. Remember, that the order in which the ratio is presented is vitally important; that is, 100:50 is not the same as 50:100. A proportion is a specific type of ratio indicating the probability or percentage of the total sample that experienced an outcome or event without respect to time. Here, the numerator of the proportion, representing patients with the disease, is included in the denominator, representing all individuals at risk. For example, say 850 non-MS patients

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are added to the sample of 150 MS patients from the example above to create a total sample of 1000 patients. Thus, the proportion of patients with MS is 0.15 or 15% (i.e., 150/1000). A rate is a special form of a proportion that includes a specific study period, typically used to assess the speed at which the event or outcome is developing.8 A rate is equal to the number of events in a specified time period divided by the length of the time period. For example, say over a 1-year study period, 50 new cases of MS were diagnosed out of the 850 previously undiagnosed individuals. Thus, the rate of new cases of MS within this sample is 50 per year.

INCIDENCE AND PREVALENCE Incidence quantifies the occurrence of an event or outcome over a specific study period within a specific population of individuals. The incidence rate is calculated by dividing the number of new events by the population at risk, with the population at risk defined as the total number of people who have not experienced the outcome. For example, consider the 50 new cases of MS that developed from the example above from the 850 originally undiagnosed individuals. The incidence rate is approximately 0.06 (i.e., 50/850). Note the denominator did not include the 150 patients already diagnosed with MS because these individuals could longer be in the population at risk. Prevalence quantifies the number of people who have already experienced the event or outcome at a specific time point. Prevalence is calculated by dividing the total number of people experiencing the event by the total number of individuals in the population. Note that the denominator is everyone in the population, not just individuals in the population at risk. For example, the diagnosed MS cases (i.e., 50 + 150 = 200) would be divided by the population that includes them. That is, the prevalence of MS in this sample is 0.20 (i.e., 200/1000). Finally, it is important to consider both incidence and prevalence when describing events or outcomes. This is because prevalence varies directly with incidence and the duration of the sickness or disease. For example, consider influenza where the duration of the sickness is relatively short. Thus, while incidence of new influenza cases may be  high the overall prevalence may be low because most individuals recover quickly. By contrast, consider individuals diagnosed with asthma. Because asthma is incurable, the prevalence of the disease may be high, whereas the incidence may be low depending on the total number of new cases diagnosed throughout the year.

RELATIVE RISK AND ODDS RATIO Relative risk is defined as the ratio (or probability) of the incidence of an event occurring in individuals exposed to a stimulus compared to the incidence of the event in those not exposed to the stimulus. Relative risk can be calculated directly from the cohort study

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design (see Chapter 5). Briefly, this design is typically a prospective observational design comparing the incidence of experiencing an event in cohorts of exposed and unexposed individuals over time. Relative risk is used when comparing the probability of an event occurring to all possible events considered in a study. For example, consider the risk of developing lung cancer in those who are exposed and unexposed to second-hand smoke over a 10-year study period. Upon study conclusion, the 2 × 2 contingency table, shown in Figure 8–7, is created containing frequency counts of events for two groups exposed and unexposed to the second-hand smoke stimulus. This table provides all data necessary to calculate the incidence of the event for both exposed and unexposed individuals. Relative risk is calculated by dividing the proportion of individuals who suffered the event in the exposed group (i.e., A/A + B) by the proportion of individuals who suffered the event in the unexposed group (i.e., C/C + D). Relative risk provides a single number ranging from zero to infinity, and there are three resulting interpretations provided below.6 1. If relative risk equals 1, the risk of experiencing the event was equal within the exposed and unexposed groups. Thus, there is no association of being exposed to the stimulus. 2. If relative risk is greater than 1, the exposed group has a greater risk of experiencing the event compared to the unexposed group. Thus, there is a positive association or detrimental effect (risk factor) of being exposed to the stimulus. 3. If relative risk is less than 1, the exposed group has a lower risk of experiencing the event compared to the unexposed group. Thus, there is a negative association or protective effect of being exposed to the stimulus. When relative risk cannot be calculated, researchers will often present an odds ratio. Odds are calculated by dividing the proportion of people experiencing an event by the proportion of people not experiencing an event. Thus, an odds ratio is a ratio of two odds; one for individuals exposed to the stimulus and the other for those not exposed to the stimulus. Odds ratios range from zero to infinity. They have three interpretations identical to those presented above for relative risk, simply substitute the odds ratio for relative risk.

Event

No Event

Exposed

A

B

A+B

Unexposed

C

D

C+D

A+C

B+D

A+B+C+D

Figure 8–7. Example of a 2 × 2 contingency table.

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Odds ratios can be calculated for both cohort and case-control designs. A case-control study compares cases that have experienced the event and controls who have not, and then assesses whether each individual was exposed to a stimulus or not. Thus, a casecontrol study is retrospective. Odds ratios are used when comparing events to nonevents with its calculation depending on the study design. For example, consider comparing a group of individuals who developed measles to those who did not and then determining whether they received all recommended vaccinations. In a cohort study, the odds ratio is calculated by dividing the odds of experiencing the event in the exposed group (i.e., A/B) by the odds the unexposed group who experienced the event (i.e., C/D). In a case-control study, the odds ratio is calculated by dividing the odds that cases were exposed to the risk (i.e., A/C) by the odds that the controls were exposed (i.e., B/D). Relative risk and odds ratios are comparable in magnitude only when the outcome under study is rare (e.g., some cancers). It is important to consider that odds ratios consistently overestimate risk when the outcome is more common (e.g., hyperlipidemia). As a result, relative risk should be used if possible and caution should be exhibited when interpreting odds ratios.

SENSITIVITY, SPECIFICITY, AND PREDICTIVE VALUES Sensitivity, specificity, and positive and negative predictive values indicate the ability of a test to identify correctly those experiencing the event and those who did not. For example, consider the ability of a blood glucose screening test to correctly identify individuals with diabetes. Four outcomes result from this test, which are required for the calculation of sensitivity, specificity, and the predictive values: 1. True positives (TP) have the disease and have a positive test result. 2. False positives (FP) do not have the disease, but have a positive test result. 3. True negatives (TN) do not have the disease and have a negative test result. 4. False negatives (FN) have the disease, but have a negative test result. Sensitivity is the probability a diseased individual will have a positive test result and is the true positive rate of the test. It is calculated by dividing true positives by all individuals who actually have the disease (i.e., TP/TP + FN). Specificity is the probability a disease-free individual will have a negative test result and is the true negative rate of the screening test. It is calculated by dividing true negatives by all disease-free individuals (i.e., TN/TN + FP). Positive and negative predictive values are calculated to measure the accuracy of the screening test. Both predictive values are directly related to disease prevalence; that is, the higher the prevalence, the higher the predictive value.6 Positive predictive value

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provides the proportion of individuals who test positive for the disease that actually have the disease. It is calculated by dividing true positives by all individuals with a positive test result (i.e., TP/TP + FP). Negative predictive value provides the proportion of individuals who test negative who are actually disease-free. It is calculated by dividing true negatives by all individuals with a negative test result (i.e., TN/TN + FN). It is important to identify the implications all four values have to new and existing research. When designing a study involving a screening test, researchers must indicate a standard cutoff score for their screening. That is, qualify who is to be considered diseased and who will be considered disease-free. This decision clearly reflects the repercussions of classifying individuals as false negatives or false positives. For example, consider a screening tool for early stage breast cancer, where there are considerable consequences for both false positives and false negatives. On the one hand, a patient with a false positive may be referred for unnecessary testing that is painful and expensive, not to mention emotionally taxing. On the other hand, a false negative has more serious implications, since the patient may not receive any treatment until the disease has progressed.

Types of Study Design The distinction between experimental, quasi-experimental, and nonexperimental research is important, both from a study design perspective and when evaluating literature. Although experimental designs are considered the gold standard by many, do not discount research conducted using quasi-experimental and nonexperimental designs, as long as the limitations are considered. Each of these types of designs will be discussed below.

EXPERIMENTAL DESIGNS In the biomedical sciences, experimental designs are typically referred to as a randomized controlled trial (RCT). A full treatment of experimental design is presented in Chapters 4 and 5, so the discussion provided here will only touch the tip of the iceberg on experimental designs. Interested readers are encouraged to consult the suggested readings provided at the end of this chapter for more information. First, experimental designs always allow the researcher to manipulate levels of an IV. For example, consider a drug trial assessing the effectiveness of a new cancer medication. For this trial, four groups of participants are randomly assigned to a different dose of a medication. Thus, there are four levels of the IV. The researcher, within ethical and theoretical constraints, can manipulate the size of the dose, if the participants will be measured multiple times, and the length of the study period.

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Second, in experimental design participants are randomly assigned to levels of the IV; thus, any participant has a chance of being placed in any single group. There are many different methods and theories of randomization and the chance of being in one group versus another does not necessarily have to be equal. For example, consider a study that has two treatment groups and one placebo group where the researcher is interested in the difference between the treatment groups. Because the difference in the DV between the placebo and either treatment group will usually be larger than the difference in the DV between the treatment groups, the researcher may randomize more participants to the treatment groups to increase statistical power to detect the difference between treatments. Note that the concept of statistical power is discussed in the Statistical Inference section below as well as in Chapter 4. Often a 2:2:1 or 3:3:1 randomization schedule will be used with two or three times as many participants, respectively, being randomized to treatment as opposed to placebo. Third, causality can be determined with proper experimental control of error. For example, the effectiveness of a cancer drug can be better explained by reducing sources of error due to the participant (e.g., age, health status), setting (e.g., prescriber’s office), or diagnostic tests (e.g., measurement accuracy). Finally, it should be noted RCTs have limitations. The primary limitation is cost, as RCTs are extremely costly requiring many considerations including space, personnel, and participants. RCTs also may have limited external validity and generalizability due to extreme control over experimental conditions (e.g., efficacy study) that do not necessarily translate to real world situations (e.g., effectiveness study). Third, it is difficult, if not impossible, to study rare events with an RCT due to ethical concerns and the considerable sample size required.

QUASIEXPERIMENTAL DESIGNS Quasi-experimental designs are used more often in the social sciences, but they can be observed in the biomedical literature. On the surface, these types of designs appear to be experimental; however, they lack one key aspect, random assignment. For example, consider examining the effectiveness of a new dialysis treatment. Most dialysis patients are already in the care of a nephrologist at a specific clinic. Because nephrologists typically see numerous patients, randomizing patients to specific levels of treatment (i.e., the IV) within a group that is under the care of the same nephrologist may be unfeasible logistically or may lead to medication errors. Thus, the entire clinic must be randomized. That is, all patients within a specific clinic will receive one treatment. Advantages of quasi-experimental designs include reduced cost and a quicker timeline compared to RCTs with the addition of possible increases in external validity due to conditions being more consistent with the real world. The disadvantages, however, are

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considerable. This is most notable with the lack of random assignment. Nonrandom assignment may create dissimilar groups based on any number of characteristics that are potentially related to the success of the treatment. For example, consistent differences in patient demographics (e.g., sickness) may result from a dialysis clinic in the suburbs being compared to a dialysis clinic in a more urban setting. Further, causation cannot be implied and statistical analysis can potentially be rendered uninformative.9

NONEXPERIMENTAL DESIGNS Nonexperimental designs have several advantages over RCTs, primarily lower cost, a quicker timeline to publication, and a broader range of participants.10 The advantages of nonexperimental studies over RCTs have prompted their widespread use in the biomedical sciences. Overall, these studies tend to be nonrandomized, retrospective, and correlational in nature and are distinct because the researcher cannot manipulate the IV(s). For example, consider a 5-year retrospective study assessing the effectiveness of statin therapy on preventing cardiac events. The researcher has knowledge of which patients initiated statin therapy, but has no control over the drug, dose, adherence, etc. Although the researcher may assign patients to groups based on dose size, the researcher cannot randomly assign patients to a specific drug nor can they manipulate the dose. In addition, nonexperimental research often fails to indicate causality, which is due to lack of experimental control and randomization as well as inability to identify all confounding variables. Finally, it is important to note that while nonexperimental designs are ubiquitous in the biomedical sciences, treatment effects may be different when compared to RCTs.11

Case Study 8–1 The human body needs vitamin D to absorb calcium. Without sufficient calcium absorption the body will extract calcium from its bone stores thereby weakening bone and increasing the probability of bone fractures. An endocrinologist interested in bone metabolism is considering a 6-month prospective study to evaluate whether differing doses of vitamin D will affect rates of calcium absorption in postmenopausal women. The researcher plans to enroll her own patients from those seen at her clinic. Calcium absorption will be measured by the dual isotope tracer method and vitamin D will be measured as serum 25-hydroxyvitamin D (25OHD). Both will be treated as continuous variables in statistical analysis. After baseline calcium absorption and 25OHD measurements are collected, participants will be randomized into four groups—one placebo group and three groups ingesting a different orally administered vitamin D supplement daily for 6 months

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(i.e., 500 international units, 2500 international units, and 5000 international units). At the end of the 6-month study period, calcium absorption will be measured again. 1. 2. 3. 4. 5. 6. 7.

8. 9.

Describe the population of interest. What sampling strategy or strategies were used? Would this study be considered a randomized controlled trial? Why or why not? What is the DV for this study? What is the scale of measurement for the DV? What is the IV for this study? What is the scale of measurement for the IV? How many levels does the IV have? What about the IV is manipulated by the researcher? Based on the study description, were any confounding variables or covariates considered for this study? The researcher is planning on using the binomial distribution to evaluate the probability the participant is calcium deficient. Is this correct given the scale of the DV? If not, what distribution would be a better option to consider? The researcher is planning on presenting calcium absorption and 25OHD as median and IQR. Is this the most appropriate measure of central tendency for these variables? A histogram of the calcium absorption variable indicated severe positive skewness, but no outliers. The researcher is considering using a square root transformation of the DV prior to analysis. Is a data transformation appropriate? Why or why not?

The Design and Analysis of Clinical Trials The U.S. National Institutes of Health (NIH) defines five different types of clinical trials— treatment, prevention, diagnostic, screening, and quality of life.12 In this chapter, two specific types of treatment clinical trials are discussed—the randomized controlled trial (RCT) and adaptive clinical trial (ACT). The experimental design of RCTs and ACTs are discussed at length in Chapters 4 and 5, so the descriptions provided in this chapter are minimal. Briefly, RCTs and ACTs are both protocol based, meaning that every step of the  study from design to analysis is identified a priori. They are prospective studies where participants are followed over time using strict experimental control to indicate reliably the causality between the DV and manipulated IV.

THE DESIGN OF CLINICAL TRIALS Parallel-Groups Design The most common RCT is a parallel-groups design, where the IV typically involves participants randomized into fixed levels of treatment, also known as treatment arms, with

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each arm indicating a different treatment or comparison.13–16 That is, participants are randomly assigned to one, and only one, treatment arm. As stated in the previous section, the sample size within each group does not have to be equal and can vary depending on estimated statistical power to detect treatment effects. There are two parallel-groups designs frequently used in the biomedical sciences— group comparison and matched pairs.15 Briefly, a group comparison design simultaneously compares at least two groups of participants after each group is randomized to a different level of the IV. In a matched pairs design, participants are matched based on one or more characteristics (e.g., age, race) and then randomized to levels of the IV.

Crossover Design The second most common RCT is a crossover design. A crossover design has the primary advantage of each participant serving as his or her own control.13,15 The primary advantage of a crossover design is that it requires fewer participants in comparison to a parallel-groups design due to the fact that at the end of the study all participants will have received all treatment arms. A disadvantage is that a crossover design cannot be used in a study where the first drug may cure the participant (e.g., an antibiotic given for an infection), since there would be no reason for the participant to crossover to the other agent. For example, consider a 1-month study that includes two treatment arms. For a parallel-groups design, say 20 participants are required; that is, 10 participants are randomized to each treatment arm. By contrast, in a crossover design, only 10 subjects are required because each participant receives both treatments—10 participants receive the first treatment and the same 10 participants receive the second treatment. While both designs have two total measurements, in the parallel-groups design, two individual groups of participants provide one measurement each, whereas in the crossover design the same group of participants provides both measurements. At this point, a common question is whether the effect of the first treatment carries over to influence the effect of the second treatment. This is a considerable concern in crossover designs and is dealt with by including a washout period between treatments. While the maximum duration of the washout period is arbitrary so long as the effect of the first treatment to be absent prior to beginning the second treatment, but not be so long that participant attrition becomes a concern.

Adaptive Design A more recent advancement to the RCT is the adaptive design or ACT. Although an ACT is possibly cheaper and more ethical than an RCT, this design is much more complex to both implement and analyze. Briefly, ACTs implement changes or adaptations in the

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design of the study based on the results of a predetermined set of interim statistical analyses. Interim analyses can be based on blinded or unblinded data, with the resulting adaptations aimed at establishing a more efficient, safer, and informative trial that is more likely to demonstrate treatment effects.17 For example, consider a 3-year study examining the effect of three different large doses of vitamin D on parathyroid hormone. Because implementing large doses of vitamin D is controversial, ethical considerations require this study to be adaptive. Here, the interim analyses would provide important information regarding the effectiveness and safety of the doses. Say, for example, that the ACT has interim analyses scheduled quarterly. Further, say that at the end of the second quarter of the first year the interim analyses indicated that the group receiving the highest dose of vitamin D had twice the risk of developing kidney stones compared to the other two groups. Thus, the group receiving the highest dose of vitamin D could have their dose reduced or the group could be dropped from the study completely. After these adaptations are implemented, the study continues as designed.

THE ANALYSIS OF CLINICAL TRIALS The analysis of clinical trials typically involves evaluating repeated measures data where participants are followed prospectively over time or conducting an endpoint analysis using only the last observation or measurement. Regardless of the study design, the choice of the appropriate statistical test to analyze these data is based primarily on the scale of measurement of the DV, but other factors should also be considered (see the Selecting the Appropriate Statistical Test section later in the chapter). Because an endpoint analysis is straightforward conceptually, a brief discussion of repeated measures design and analysis considerations is presented. Using a repeated measures design allows researchers to study the treatment effects over time in a smaller sample of participants due to the increased statistical power. Briefly, a repeated measures design increases statistical power by removing the error variance due to the participant. That is, each participant serves as their own control. Removing error variance is important because it results in larger test statistics and an increased probability of achieving statistical significance. However, a repeated measures design has limitations relevant to clinical trials, primarily participant attrition and nonadherence. Attrition and nonadherence can assume many forms in a clinical trial. For example, participants may drop out of the study, fail to complete all required measurements, receive incorrect treatment or doses, or a myriad of other possible protocol violations. Thus, the first part of this section will discuss how the analysis of clinical trials typically handles missing data due to attrition and nonadherence. The final sections discuss the analysis of parallel-group, crossover, and adaptive designs.

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Intent-to-Treat and Per-Protocol Approaches Two analytical approaches exist for clinical trials—intent-to-treat (ITT) and perprotocol (PP). The ITT approach is often employed in the presence of violations to protocol or patients being lost to follow-up. ITT is the approach most often used in the biomedical literature. ITT requires the analysis to include all participants in the arm to which they were randomized originally. That is, treatment effects are best evaluated by the planned treatment protocol rather than the actual treatment given.18 For example, consider a participant randomized to receive treatment A but instead receives treatment B. For analysis, this participant would be considered as receiving treatment A. It should be noted that if a large number of protocol violations of this nature occur, the study would be discontinued; thus, these occurrences are relatively rare. It is important to note that for ITT has considerable weaknesses. Most notably, for ITT to be unbiased, attrition and nonadherence are considered to occur completely at random.14 However, this is an untestable hypothesis. Further, the ITT approach can dilute treatment effects simply by including nonadherent participants by employing the last observation carried forward (LOCF) technique discussed later in this section. By comparison, the PP approach evaluates only compliant participants with complete data. Although this analysis is straightforward analytically and allows researchers to evaluate a more accurate treatment effect, it has substantial limitations, primarily, reduced statistical power compared to an ITT approach, because participants with incomplete data are not considered in analysis.18 Because the ITT approach considers all participants with at least one measurement, an imputation or replacement method is often employed for missing measurements. The LOCF technique is one of the most commonly used imputation methods. This method uses the last recorded measurement for a participant for every missing measurement. For example, consider a study measuring HbA1c measured on six occasions over a 1-year study period. If a participant had only the first three measurements, the third (i.e., last) measurement would be imputed for measurements four through six. 5 From this example, it is clear the LOCF technique may not only dilute treatment effects, but also introduce significant bias into the results of statistical tests. Therefore, be cautious when evaluating a study that has employed the LOCF technique. As a result of these limitations, better imputation approaches have been suggested including multiple imputation and the use of maximum likelihood estimation. These techniques are beyond the scope of this chapter, but are valid and produce unbiased estimates if data are considered missing at random.19,20

Analyzing Parallel-Groups Designs When analyzing a parallel-groups design, the traditional approach is to conduct an endpoint analysis using only the final measurement. If the DV is continuous, analysis will

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typically require an independent samples t-test for two groups, one-way analysis of variance (ANOVA) for more than two groups, or analysis of covariance (ANCOVA) for two or more groups, statistically controlling for a baseline DV measurement. If the DV is categorical, an endpoint analysis will typically require a chi-square test or logistic regression analysis. Note that these analyses are discussed in detail in the Statistical Tests section later in the chapter. For example, consider a study designed to assess the effect of lubiprostone compared to placebo in treating chronic constipation associated with Parkinson disease. Following randomization, this 1-month study will assess constipation symptoms twice—at the end of the second and fourth week. An endpoint analysis would only consider the treatment effect at the end of week 4, ignoring the measurement at the end of week 2. Thus, it is clear that this type of analysis does not consider the repeated measures, and, as a result, does not consider the changes occurring over time. Further, an endpoint analysis does not take full advantage of statistical power increases from a longitudinal design as discussed in the Design and Analysis of Clinical Trials section. By contrast, to assess for change over time using repeated measures for a continuous DV, researchers often employ a mixed between-within ANOVA or mixed-effects linear regression. Briefly, these analyses assess differences between treatment groups as well as changes over time within treatment groups. The major benefit of these analyses is provided by the interaction effect, which evaluates whether the change over time was different between the two treatment arms (see Figure 8–8). To evaluate group differences in change over time for a categorical DV, researchers are required to employ a mixed-effects logistic regression. This analysis allows the researcher to evaluate group differences and interaction effects. In general, mixed-effects analyses are extremely complex and even a brief overview of this analysis is well beyond the scope of this chapter. With that said, when these analyses are most appropriate is provided in the Selecting the Appropriate Statistical Tests section. Interested readers are encouraged to consult the suggested readings at the end of the chapter for a treatment of this analysis. As an example of analyzing a parallel-groups design, reconsider the lubiprostone example. Say the results are presented graphically in Figure 8–8. Notice that two groups experience drastically different change in constipation symptoms between the end of week 2 and the end of week 4. The between-group difference in constipation symptoms over time is the interaction effect. Because a lower number of symptoms are indicative of treatment success, Figure 8–8 shows that the effect of lubiprostone is more effective compared to placebo over the study period. Following a statistically significant interaction effect, researchers can conduct followup or post hoc tests to determine where the significant difference occurred. In Figure 8–8, there is likely no statistically significant difference in constipation symptoms between groups at the end of week 2, but there is a likely statistically significant difference at the

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4

Control Treatment

Constipation symptoms

3

2

1

0 End of week 2

End of week 4

Figure 8–8. Statistically significant interaction effect.

end of week 4. Therefore, post hoc tests can assist researchers in identifying the shortest treatment time or minimum effective dose by indicating where treatment effects diminish (e.g., the slope plateaus) or indicating when differences between doses converge and are no longer statistically significant.

Analyzing Crossover Designs The purpose of the crossover design is to study treatment effects using the participant as his or her own control. Remember, in a crossover design each participant receives all treatment arms, with an adequate washout period occurring between arms to prevent the carryover of treatment effects. For example, consider the lubiprostone example described in the Analyzing Parallel-Groups Designs section. Instead of having two treatment arms as in a parallel-groups design, the crossover design would randomize participants into a different treatment order. That is, Group A would receive lubiprostone and Group B would receive placebo for the first 2 weeks. At the end of the 2-week study period, constipation symptoms are assessed. Next, all participants are required to have a 3-week washout period purported to effectively eliminate any carryover effects of the lubiprostone. Note that during the washout period the placebo is not given either. After the washout period, Group A would receive placebo and Group B would receive lubiprostone for 2 weeks. At the end of this second 2-week study period, constipation symptoms are assessed again.

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Statistically, a crossover design requires an initial test for order effects. If the DV is continuous, order effects are assessed by evaluating the interaction effect between order of treatment and the IV using a mixed between-within ANOVA or a mixed-effects linear regression. These analyses are described in detail in the Selecting the Appropriate Statistical Test section later in the chapter, but for now consider both analyses useful when evaluating interaction effects. When testing for order effects, a statistically significant interaction indicates that the order in which the treatments were received influenced the treatment effect. For example, say that receiving lubiprostone prior to placebo had a different treatment effect than receiving placebo prior to lubiprostone. A clear order effect is presented in Figure 8–9. Notice the effect of placebo differs depending on the order in which it was received. The presence of a statistically significant order effect can have multiple explanations. Considering the example and Figure 8–9, it is clear the washout period may not have been long enough, as the effectiveness of lubiprostone carried over to measurement of the placebo. In addition, the groups may have been initially different following randomization. Whenever a statistically significant order effect is identified, no further analysis is conducted as any subsequent analyses are biased by this order effect. However, if the interaction is nonsignificant and the DV is continuous, an endpoint analysis is typically evaluated via paired-samples t-test (also explained in detail in the Selecting the Appropriate Statistical Test section later in the chapter). That is, treatment differences between lubiprostone and placebo are assessed without respect to the order in which the treatments were received.

Constipation symptoms

4

Order Lubiprostone-placebo Placebo-lubiprostone

3

2

1

0 Lubiprostone

Placebo

Figure 8–9. Statistically significant order effect.

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Analyzing Adaptive Designs The statistical analyses and considerations used when analyzing adaptive designs are similar to parallel-groups and crossover designs. If the DV is continuous, statistical tests used during the interim analyses or on the study endpoint typically include an independentsamples t-test for comparing two groups, one-way ANOVA for comparing more than two groups, or ANCOVA for statistically controlling baseline DV measurement. Note that the DV for the interim analyses is the most recent measurement. If the DV is categorical, an interim analysis will typically require a chi-square test or logistic regression analysis. All of the analyses mentioned are discussed in detail in the Statistical Tests section later in chapter. Several important considerations are required when analyzing and interpreting the results from adaptive designs.17 First, all interim and endpoint analyses suffer the potential risk of inflated Type I errors. Briefly, a Type I error can be thought of as a false positive. That is, the statistical test could indicate a statistically significant result when the result is actually not significant. Type I error has been discussed in Chapter 4 and in the Statistical Inference section later in this chapter. With this definition in mind, as the number of interim analyses increase the probability of finding a false positive might increase as well. To adjust for this possibility, researchers will often make the criteria for achieving statistical significance more conservative by adjusting alpha (see the Statistical Inference section later in chapter for a full description of alpha). Note that adjusting alpha is not a ubiquitous practice and there is no universal recommendation for doing so. Just be aware that inflated Type I errors may be an issue in an ACT. Second, estimates of population parameters may be biased. That is, any adaptation can reduce the generalizability to the original population sampled, produce underestimated or overestimated parameter estimates, and produce misleading confidence intervals. Researchers must carefully document and provide rationale for adaptations resulting from interim analysis. Failure to do so will indicate results should be viewed with extreme caution. Finally, when all adaptations are considered, the overall results of the endpoint analyses may actually be invalid, providing inaccurate support for treatment effects. Consumers of research are urged strongly to consider these factors when interpreting and evaluating research using adaptive designs.

Statistical Inference Inferential statistics provide the probability a difference or association is actually observed in the population based on the analysis of sample data. Inferential statistics allow researchers to make rational decisions in the presence of random processes and variation. This section presents several requirements that need to be considered prior to conducting and

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evaluating the result of a statistical test. First, the sampling distribution and application of the central limit theorem are discussed, followed by hypothesis testing, as well as Type I and Type II errors and statistical power. Then, the difference between statistical and clinical significance is presented. Finally, the appropriate uses of parametric and nonparametric statistical tests are provided as well as a brief description of degrees of freedom.

SAMPLING DISTRIBUTIONS AND THE CENTRAL LIMIT THEOREM Statistical inference uses sampled data to make conclusions about a specific population. Because quality samples are chosen randomly, the means produced from these samples are also random.21 Given this information, it is important to remember that the mean may not be exactly representative of the population and will vary from sample to sample. However, the law of large numbers states as the size of the sample increases, the sample mean will move closer to the population mean. Further, as the number of samples increases, the mean of the sample means will begin to approximate the population mean. 6 The central limit theorem states when equally sized samples are drawn from a non-normal distribution, the plotted mean values from each sample will approximate a normal distribution as long as the non-normality was not due to outliers. A distribution of the sampled means calculated from repeated samples is termed the distribution of sampling means. For example, consider a study to analyze the mean value of blood urea nitrogen (BUN) in the general, healthy population, where the researcher selects 100 random samples of 10 healthy participants. Each sample of 10 will provide a mean BUN value. Although mean BUN will vary from sample to sample, when the 100 sample means are plotted in a histogram, this distribution of sampling means will begin to approximate the actual population distribution. The central limit theorem states sufficiently large samples should approximate a normal distribution of sampling means as long as the data do not contain outliers. A sufficiently large sample is generally considered to consist of 30 or more participants or a situation where the degrees of freedom for the statistical test are greater than 20.22 Note that degrees of freedom are discussed later in this section. In addition, researchers must be careful not to confuse the issue of having a large enough sample to achieve statistical significance and a large enough sample to be representative of the population. As with any normal distribution, the standard deviation of the distribution of sampling means can be calculated. This is termed the standard error of the mean (SEM). The SEM is equal to the standard deviation divided by the square root of the sample size, and reflects variability within the sample means. Further, standard error is used in the majority of statistical tests. It is important when evaluating the literature to understand the relationship between the standard deviation and the SEM. Researchers often present the SEM to show variability or noise in their data. Note that the SEM will always be smaller

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than the standard deviation. Thus, the use of SEM will suggest the data is less variable and often more appealing.

HYPOTHESIS TESTING A hypothesis indicates a theory about the population regarding an outcome the researcher is interested in studying. The statistical analyses discussed in this chapter evaluate two types of hypotheses, the null hypothesis and the alternative or research hypothesis. That is, the analyses discussed employ procedures generally known as null hypothesis significance testing (NHST). The null hypothesis (H0) assumes no difference or association between the different study groups or variables, whereas the alternative hypothesis (HA or H1) states there is a difference or association between the different study groups or variables. A representative, ideally random, sample is then drawn from the population of interest to estimate the difference or relationship and test whether this difference or relationship rejects or fails to reject the null hypothesis. It is important to note that failing to reject the null hypothesis does not indicate the null hypothesis is true. This is a common misconception observed frequently in the literature. There are often many other reasons for failing to reject the null hypothesis including inadequate experimental design, inadequate control over extraneous variables, and inadequate sample size to detect the effect of interest, among others. When testing a specific hypothesis, a researcher is required to determine whether their hypothesis is directional or not. A directional hypothesis requires a one-tailed hypothesis test, whereas a nondirectional hypothesis requires a two-tailed hypothesis test. For example, consider a hypothesis which states that initiating statin therapy will lower low-density lipoproteins (LDL). Note that use of the term lower implies directionality and requires a one-tailed test. If, however, the researchers were looking for any effect of statin therapy, whether lowering or raising LDL, the hypothesis is nondirectional and would require a two-tailed test. In the literature, it is generally more acceptable to use a two-tailed test, even if the hypothesis is directional because a two-tailed test is more conservative statistically, thereby reducing the probability of a spurious statistical significance.

ERROR AND STATISTICAL POWER It is essential that researchers establish how much error they are willing to accept before initiating a study. NHST can only result in four possible outcomes, which can be observed in Table 8–2. Type I and Type II have been discussed at length in Chapter 4, but briefly, a Type I error occurs when a statistical test rejects the null hypothesis by indicating a statistically significant difference when, in fact, the null hypothesis is true (i.e., false positive). A Type II error occurs when the researcher fails to reject the null hypothesis by not indicating a statistically significant difference when, in fact, the null hypothesis is false (i.e., false negative). Type I and Type II errors are interconnected; that is, as the probability

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TABLE 82. FOUR POSSIBLE OUTCOMES OF NULL HYPOTHESIS SIGNIFICANCE TESTING NHST Truth Decision

False H0

True H0

Reject H0 Fail to reject H0

Correctly reject H0 Type II error

Type I error Correctly fail to reject H0

of one error increases the other decreases. Researchers must consider these two errors carefully when designing studies, weighing whether a false positive is more or less concerning than a false negative. Statistical power was developed as a method allowing researchers to calculate the probability of finding a statistically significant result, when, in fact, one actually exists. This topic has been discussed in Chapter 4. Essentially, increasing statistical power reduces the probability of committing a Type II error; however, it can also increase the probability of committing a Type I error as described in the previous paragraph. Statistical power is influenced by four factors: alpha defined as the probability value at which the null hypothesis is rejected, effect size defined as the size of the treatment effect (discussed later), error variance defined as the precision of the measurement instrument, and the sample size. Statistical power can be increased by increasing alpha, effect size, or sample size as well as by decreasing error variance. Note that statistical power of 0.80 has been defined as adequate.23 However, some researchers use 0.90 or higher in the biomedical sciences, indicating that a false negative is more detrimental than a false positive, such as when evaluating the effectiveness of a novel breast cancer treatment.

STATISTICAL VERSUS CLINICAL SIGNIFICANCE Alpha and p Values The next step in the research process is to employ a statistical test to assess whether a difference or relationship is due to random variation. The researcher is interested in determining whether the observed difference or relationship rejects or fails to reject the null hypothesis. Alpha (α) is the conventionally designated decision criterion for rejecting the null hypothesis and ranges from 0 to 1. Alpha is defined as the theoretical probability of rejecting the null hypothesis conditional on the null hypothesis being true. Alpha does not represent the exact Type I error rate; instead, alpha is the upper bound of the Type I error rate. Although most studies typically set alpha at 0.05, this value is arbitrary. A more conservative (i.e., α = 0.01) or liberal (α = 0.10) alpha may be used in an attempt to show greater support for rejecting or retaining the null hypothesis, respectively. As an example, conservative alpha levels are often used to protect against Type I errors, whereas liberal alpha values are often used in drug equivalency trials where researchers are using data to show nonsignificant differences between the drugs.

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Conceptually related to alpha is the probability value (i.e., p value). Statistical tests produce p values that range from 0 to 1. The formal definition of a p value is the probability of obtaining a test statistic as large as or larger than the one obtained, conditional on the null hypothesis being true. Graphically, a p value is directly indicative of the area under the probability distribution used by the statistical test. Note that the area under any proper probability distribution is 1. A quick glance at the appendices of any introductory statistics textbook will provide area under the curve values for various distributions. In more general terms, p = 0.05 indicates 5% of the distribution’s area is to the left or right of the associated test statistic depending on whether it is positive or negative. For example, consider a one-tailed statistical test with a positive test statistic and reconsider Figure 8–5. A z-score of 1.645 leaves approximately 5% of the distribution to the right of this value. This example highlights how a p value less than 0.05 indicates that less than 5% of the values (or area) lies beyond a specific test statistic value. As an alternative example, consider a two-tailed (i.e., nondirectional) statistical test. A z-score of 1.96 leaves approximately 2.5% of the distribution to the right this value, whereas a z-score of −1.96 leaves approximately 2.5% of the distribution to the left of this value. Thus, a two-tailed test also leaves 5% of the area under the distribution when the two parts are aggregated. Regardless of whether a one- or two-tailed test was used, in general, if a p value is less than the specified alpha, the researcher rejects the null hypothesis and the difference or relationship is considered statistically significant. Alternatively, if the p value is equal to or greater than alpha, the researcher has failed to reject the null hypothesis and the difference or relationship is not considered statistically significant. 7 Be aware that there is continuing difficulty when interpreting p values, even among statisticians.24 Therefore, it is important to be cognizant of several misconceptions about p values that are stated commonly in the literature. First, a p value is not the exact probability of committing a Type I error. Second, the p value is not the probability that the null hypothesis is true, nor is 1 − p the probability that the alternative hypothesis is true. Third, a small p value is not evidence that the results will replicate nor can p values be compared directly across studies. Fourth, a p value indicates nothing about the magnitude of a difference or relationship. For example, a small p value (e.g., p = 0.00001) does not indicate a larger treatment effect than a larger p value (e.g., p = 0.049). Finally, for better or worse, alpha in NHST is treated like a cliff. For example, if alpha is set at 0.05 and a p value of 0.051 is obtained, a researcher will often state that a p value of 0.051 was trending toward significance or that the p value indicated marginal or moderate significance. These statements are often wholly incorrect! Thinking back to the discussion of one-tailed versus two-tailed significance tests provided in the Hypothesis Testing section, trending can only occur if the hypothesis is directional using a one-tailed test and the result obtained was in the hypothesized direction. Results can never be trending toward significance if the hypothesis was nondirectional using a two-tailed test because the alternative claim that the result was trending away from significance cannot be challenged.

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Confidence Intervals Statistical significance can also be established by calculating a confidence interval around the estimated population parameters (e.g., sample means, slopes) or test statistics (e.g., t). A confidence interval provides a range of scores likely to contain the unknown population parameter, and generally, a confidence interval is reported using a 95% confidence level. The calculation of confidence intervals includes both sample size and variability where smaller confidence intervals indicate less variability in the data. A 95% confidence interval is calculated by multiplying 1.96 by the SEM and adding or subtracting this value from the estimated parameter to find the upper and lower confidence limits, respectively. A 95% confidence interval indicates that if repeated random sampling occurs within the population of interest under consistent conditions (e.g., sample size), the true population parameter would be included in the interval 95% of the time. Thus, every value within the interval is considered a possible value of the population parameter. Confidence intervals can be used it indicate statistical significance without the use of statistical tests. This process varies according to whether the researcher is examining population parameters or test statistics. For example, consider Figures 8–10, 8–11, and 8–12. In each figure, HbA1c values are being compared for a treatment and placebo group. Further, mean HbA1c for each group is presented as the circle, while the 95% confidence intervals are the whiskers extending above and below the means. The overlap of the confidence intervals between groups is directly related to p values; that is, less overlap is indicative of larger differences resulting in smaller p values. In Figure 8–10, notice that the confidence intervals do not overlap; thus, this difference can be assumed statistically significant at least at p < 0.05. Statistical significance can also be indicated 10 9

HbA1c (%)

8 7 6 5 4 Placebo

Treatment

Figure 8–10. Statistically significant result indicated by non-overlapping 95% confidence intervals.

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10 9

HbA1c (%)

8 7 6 5 4 Placebo

Treatment

Figure 8–11. Statistically significant result indicated by overlapping 95% confidence intervals.

when the confidence intervals overlap as long as the overlap is less than approximately 50% of a whisker, as in Figure 8–11.25 Finally, as shown in Figure 8–12, substantial overlap in confidence intervals indicates a nonstatistically significant difference (i.e., p > 0.05). Determining statistical significance using confidence intervals around test statistics (e.g., t) uses procedures that vary based on the statistical test employed. For most parametric tests of group differences and correlation, a 95% confidence interval around the test statistic containing zero is not considered statistically significant at an alpha of 0.05. 10 9

HbA1c (%)

8 7 6 5 4 Placebo

Treatment

Figure 8–12. Overlapping 95% confidence interval indicating a nonsignificant result.

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That is, statistical significance for these types of analyses are essentially testing that the differences or relationship are different from zero (i.e., H0 = 0). Thus, a 95% confidence interval containing zero essentially indicates that it is plausible the true population difference or relationship could be zero.26 For example, consider the commonly used independent-samples t-test to evaluate for a difference between two group means (this statistical test is discussed in the Statistical Tests section below). Say that the test statistic produced was 2.0, but the confidence interval ranged from −0.50 to 4.50. Based on this sample, the difference would not be considered statistically significant because it is plausible that in 95% of samples of the same size the true population parameter could in fact be zero. Alternatively, the 95% confidence interval for test statistics based on ratios (e.g., odds ratios) that contain 1 are not considered statistically significant at an alpha of 0.05. Remember from the Epidemiological Statistics section, that an odds ratio or relative risk of 1 indicates no difference. Thus, a 95% confidence interval for a relative risk or an odds ratio that contains 1 indicates that it is plausible the true population parameter could in fact be an odds ratio or relative risk of 1. For example, say the result of a logistic regression analysis produced an odds ratio of 2.5 (this statistical test is discussed in the Statistical Tests section below). This value is greater than 1 indicating an increase in odds of experiencing the event. However, say the 95% confidence interval ranged from 0.50 to 15.0. Because the confidence interval contains 1, the odds ratio of 2.5 is not statistically significant using an alpha of 0.05.

Clinical Significance and Effect Size 8 When evaluating the significance of the finding, keep in mind that statistical significance (e.g., p < 0.05) does not indicate clinical significance. Statistical significance can be manipulated in several ways, most easily by increasing sample size drastically. A sample size increase may artificially reduce error variance, which in turn reduces the standard error on which the test statistic is based. Reducing the standard error increases the value of the test statistic necessarily resulting in a smaller p value. As an example, using a sample of 10,000 patients, researchers may find a CCB reduced blood glucose significantly in Type 1 DM patients. However, on examining the estimated parameters, the statistically significant difference in blood glucose was only 2 mg/dL, a decrease considered clinically insignificant. This example highlights the importance of identifying and interpreting the clinical significance or effect size of all studies. While a complete discussion of effect size is beyond the scope of this chapter, larger effect size values are always preferred. Briefly, two different types of effect sizes exist. First, standardized difference effect sizes indicate a standardized difference between groups in standard deviation units. Examples commonly seen in the literature include Cohen’s d, Glass’ Δ, and Hedges’ g.

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As indicated by their name, standardized difference effect sizes are used when evaluating mean differences between groups. Further, because the effect sizes are standardized it may be easier to think of them as z-scores. Standardized difference effect sizes range from – ∞ to + ∞, with larger absolute values indicating larger effects. Second, effect size may also be reported as the proportion of variance explained. That is, how much of the reason a participant had a specific value of the DV is due to the IV. Examples commonly observed in the literature include R2, ω2, and η2. Proportion of variance effect sizes is specific to tests of relationships or association as described in the Statistical Tests section below. Their values range from 0 to 1, with higher values indicating larger effects. Given this information, it is important to remember that the definition of clinical significance varies by substantive area; thus, the definition of clinically significant to a bench researcher may be qualitatively different from an evidence-based practitioner. Finally, not all studies will provide an effect size estimate, especially in the biomedical sciences. Thus, research must be viewed with warranted skepticism until it can be determined whether the statistically significant difference or relationship is clinically meaningful.

PARAMETRIC VERSUS NONPARAMETRIC TESTING The primary difference between parametric and nonparametric statistical tests is that parametric tests make assumptions regarding the descriptive characteristics of the normal distribution (i.e., mean, variance, skewness, and kurtosis). Nonparametric tests make a few or no distributional assumptions. In general, parametric tests are used only for interval and ratio scales, whereas nonparametric tests can be employed for any scale of measurement. Regardless, if the DV is measured on a continuous scale, the decision of which statistical test to employ typically begins with parametric tests. Because they assume a normal distribution, all parametric tests have several conservative and easily violated assumptions. These assumptions are testable, but vary depending on the statistical test employed. Therefore, in the Statistical Tests section below, all assumptions are described for each statistical test. It is important to know that employing a parametric test in the presence of a nondefined distribution will lead to inaccurate, biased, and unreliable parameter estimates. Therefore, when reading a journal article, if the authors neglect to provide information regarding assumption testing, caution should be exercised as it is unknown how much bias exists in their results. Some assumption violations challenge the robustness of parametric tests greater than others. Robustness is defined as the ability of the statistical test to produce correct inferences in the presence of assumption violations. In most situations, any assumption violation requires the researcher to employ a nonparametric test, and most parametric tests have a widely used nonparametric alternative. Most, but not all, nonparametric

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statistical tests are distribution free, meaning they do not make inferences based on a defined probability distribution. Further, additional strengths of nonparametric tests include the ability to assess small sample sizes and the ability to assess data from several different populations.27 However, it must be noted if the assumptions of a parametric test are tenable, parametric tests have greater statistical power to detect real effects compared to their nonparametric alternative(s).28

DEGREES OF FREEDOM Degrees of freedom (df) are a vital component of all statistical tests, as most probability distributions, and statistical significance are based on them. Degrees of freedom are provided for all statistical tests and are a useful indicator of adequate sample size in the presence of assumption violations. For example, recall that the central limit theorem states the distribution of sampling means is approximately normal with degrees of freedom greater than 20. Thus, the central limit theorem operates independently of the distribution of the actual raw data. Therefore, if a researcher indicates that degrees of freedom for the statistical test are greater than 20, the results can typically be viewed as robust. The definition of degrees of freedom is obscure and beyond the scope of this chapter; however, a brief description is provided. Degrees of freedom are conceptually defined as the number data points that are free to vary. Not all data is free to vary because values may have to be fixed by a specific sample parameter, such as the group mean. For example, consider a group of three participants who have a mean age of 50. Because the mean is 50, two of the participants can be of almost any age greater than zero. The third participant, however, must be the age that creates the mean of 50. Thus, if participant A is 40 and participant B is 45, participant C must be 65. That is, [40 + 45 + 65]/3 = 50. If instead, participant A is 75 and participant B is 40, then participant C must be 35. Therefore, because the age of two of the three participants could be just about any age greater than zero, there are two degrees of freedom (i.e., 2df) Calculating degrees of freedom become increasingly complex in accordance with the complexity of the statistical test. That is, degrees of freedom for bivariate tests, such as those evaluating the difference between two group means, are easier to calculate and conceptualize than multivariate tests with multiple DVs. For the analyses described in the Statistical Tests section later in chapter, calculation of degrees of freedom is not explained explicitly, but it is important to note the distribution, probability, and statistical significance of all statistical tests are based on degrees of freedom. Further, degrees of freedom will usually be subscripted next to the test statistic for all parametric tests as well as for some nonparametric tests, such as the chi-square test. Subscripted degrees of freedom should be provided in the brief results section for all statistical tests described.

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Selecting the Appropriate Statistical Test While the information presented in this chapter has outlined the underlying processes of most statistical tests used in the biomedical sciences, the remainder of the chapter uses all previous information as a base to begin to integrate more directly useful information. This section presents decision trees that allow for determination of whether the appropriate statistical test was used in a journal article. 9 The selection of the appropriate statistical test is based on several factors including the specific research question, the measurement scale of the DV, distributional assumptions, the number of DV measurements as well as the number and measurement scale of IVs and covariates, among others. Tables 8–3 and 8–4 provide decision trees to identify the most appropriate statistical test based on the unique set of factors for statistical tests of group differences and statistical tests of association, respectively. The first factor that needs to be addressed when selecting the most appropriate statistical test is whether the research question is phrased to evaluate differences or associations. In general, the last paragraph of the Introduction to any journal article should explicitly state the research questions, so give close attention to the phrasing of these questions. This may seem mundane, but it is important to note that all parametric tests of group differences are mathematically equivalent to parametric tests of association. Therefore, various parametric statistical tests can often be used interchangeably to answer the same research question. This can be seen in Tables 8–3 and 8–4 where one-way ANOVA can be used in the same situations as simple linear regression analysis. The overall statistical inference would be identical. This can be an overwhelmingly confusing concept for those with more novice statistical backgrounds. However, in general, when a research question is phrased to evaluate group differences (e.g., to determine whether three treatment groups differ on some outcome) follow the decision tree provided in Table 8–3, and when a research question is phrased to evaluate associations between variables (e.g., to determine whether a patient’s age is associated with some outcome), follow Table 8–4. The second factor to consider is the measurement scale of the DV. This is a much more concrete concept, but requires a thorough understanding of the four scales of variable measurement. Remember, nominal and ordinal scales are roughly classified as discrete or categorical, whereas interval and ratio scales are classified as continuous. Although it is often inappropriate, continuous variables may be also categorized into discrete variables. For example, categorization often occurs in the biomedical sciences with variables such as blood pressure, where exact systolic or diastolic blood pressure values are combined and categorized into low, normal, or high blood pressure. The distributional assumptions of a statistical test involve complex explanations beyond the scope of this chapter. However, there are a few concepts to remember regarding

TABLE 83. CHOOSING THE MOST APPROPRIATE STATISTICAL TEST OF GROUP DIFFERENCES DV Scale Differences from known population

Between-group differences

Within-group differences

Distributional Repeated DV Number of DV Number Covariates Appropriate Assumptions Met Measurements Measurements of IVs IV Levels Allowed Statistical Test

395

⎧ ⎪ ⎪ ⎩

Continuous Continuous Dichotomous Continuous

Yes Yes

No No No No

1 1 1 1

0 0 0 0

⎧ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎩

Continuous Ordinal or higher Ordinal or higher Continuous Ordinal or higher Continuous Continuous

Yes No No Yes No Yes Yes

No No No No No No No

1 1 1 1 1 1 1

1 1 1 1 1 1 ≥2

⎧ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎩

Continuous Ordinal or higher Ordinal or higher Continuous Ordinal or higher Continuous Dichotomous Dichotomous

Yes No No Yes No Yes

Yes Yes Yes Yes Yes Yes Yes Yes

2 2 2 ≥2 ≥2 ≥2 2 ≥2

0 0 0 0 0 1 0 0

2 2 2 ≥2 ≥2 ≥2 ≥2

≥2

No No No No

One-sample z test One-sample t-test Binomial test Kolmogorov-Smirnov test

No No No No No Yes No

Independent-samples t-test Mann-Whitney test Median test One-way ANOVA Kruskal-Wallis test ANCOVA Factorial ANOVA

No No No No No No No No

Paired-samples t-test Signed-rank test Sign test One-way RM-ANOVA Friedman test Mixed BW-ANOVA McNemar test Cochran Q test

396 TABLE 84. CHOOSING THE MOST APPROPRIATE STATISTICAL TEST OF ASSOCIATION DV Scale One-sample association

⎧ ⎪ ⎩

Categorical Categorical Categorical

Correlation and regression

⎧ ⎪ ⎪ ⎪ ⎪ ⎪ ⎩

Continuous Continuous Continuous Continuous Dichotomous Dichotomous

Within-group regression

⎧ ⎩

Time-to-event Reliability

Distributional Assumptions Met

Number of IVs

IV Scale

Covariates Allowed

Appropriate Statistical Test

1 1 1

Categorical Categorical Categorical

No No Yes

Chi-square test Fisher’s exact test Mantel-Haenszel test

Yes No Yes Yes

1 1 1 ≥2 1 ≥2

Continuous Continuous Any Any Any Any

No No No Yes No Yes

Pearson’s correlation Spearman’s rank order correlation Simple linear regression Multivariable linear regression Simple logistic regression Multivariable logistic regression

Continuous Dichotomous

Yes

≥0 ≥0

Any Any

Yes Yes

Mixed-effects linear regressiona Mixed-effects logistic regressiona

⎧⎩

Continuous

Yes

≥2

Yes

Cox proportional-hazards model

⎧⎩

Nominal

0

No

Kappa

a Mixed-effects linear and logistic regression models are complex analyses beyond the scope of this chapter. Therefore, they are not described in the Statistical Tests section. However, be aware that these analyses are available. Also, note that there is varying terminology used among researchers when describing these types of models; thus, in the literature mixed-effects models may be termed multilevel, hierarchical, nested, random effects, random coefficient, or random parameter models. The actual procedure for conducting the analyses remains identical regardless of the label attached to them.

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distributions, most of which have been described in the Common Probability Distributions section above. First, distributional assumptions are required for all statistical tests using a continuous DV. Remember, the search for the most appropriate statistical test usually begins with parametric options, and all parametric statistical tests require a normal distribution (or application of the central limit theorem). Second, the distribution of the actual DV data is never considered when assessing distributional assumptions. Distributional assumptions are based on residual values, which represent the difference between the outcome predicted by the statistical model and the actual, observed outcome. Residuals are discussed in detail later in the simple linear regression analysis section. Third, if the distributional assumptions are violated, two options are generally available. First, the researcher could use a more appropriate distribution (see Table 8–1) or, second, they could employ a nonparametric statistical test. It is also common to see data transformation to attempt to force a normal distribution, but the considerable downside of this archaic technique was discussed previously in the Transforming Non-Normal Distributions section. It is more common in the biomedical literature to see a nonparametric test used, but be aware more statistically savvy researchers will use alternative distributions if the distributional assumption is violated. The number of DV measurements is critically important to determine whether an appropriate statistical test was used. Note that studies using one DV measurement are known as cross-sectional, whereas studies using two or more DV measurements are known as longitudinal. If the DV was measured on multiple occasions, there is inherent association or correlation across DV measurements. That is, DV values from the same person inherently have a higher correlation compared to DV values from different people. Therefore, if a statistical test does not account for this correlation, the standard errors will be biased and improper statistical inference will occur. It is also critically important to consider the number of IVs and covariates, their scale of measurement, the number of categorical IV levels, and whether the IVs and/or covariates interact. Note that the distribution of the IV or covariate is never considered in the statistical analysis. With that said, the scale of measurement for each IV and covariate is important when deciding which statistical test to employ. In general, ANOVA will usually be employed for categorical IVs, whereas regression analysis is required for continuous IVs. Note that the wording of the last sentence was chosen specifically. That is, although it was stated earlier that ANOVA and regression are mathematically equivalent, ANOVA cannot be used with continuous IVs. However, regression analysis can be used with any combination of IVs and covariates measured on any scale. This can be a confusing distinction. Another important concept is the number of levels for each categorical IV. Remember, a level can be thought of as the number of groups being studied and evaluated. When testing group differences, researchers will typically employ a t-test for two levels and ANOVA for three or more groups. Determining the number of levels is important because with three or more groups, the statistical test is an omnibus test. That is, an overall test

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result will be provided indicating a difference between at least two of the groups, but the test will not indicate specifically which groups differ. The concept of an omnibus test is discussed throughout the Statistical Tests section later in the chapter. Finally, whether an IV interacts with another IV or covariate is critically important. It is important to note that ANOVA can handle interactions between categorical IVs. Remember, an interaction indicates that the value of one IV is dependent on the value of another IV. For example, treatment group differences may be smaller in older patients (i.e., a treatment group-by-age interaction). However, if there is an interaction between a categorical IV and a continuous covariate, an ANOVA-type analysis, such as ANCOVA, cannot be used and a form of regression analysis must be used instead. Whether an interaction exists is an empirical question that is testable and described in the ANCOVA section later in the chapter. Finally, other factors exist when determining whether the appropriate statistical test was used, but many are beyond the scope of this chapter. One key factor worth considering, however, is based on the concept of clustering (also known as nesting). Classic examples of clustering include children nested within the same classroom or patients nested within the same doctor. Clustering creates statistical issues that are similar to using a cross-sectional analysis on longitudinal data. That is, DV measurements from children nested within the same classroom have greater associations compared to DV measurements from children in different classrooms. Failing to account for clustering will result in biased standard errors and incorrect statistical inference. Although a description of the statistical analysis for clustered data is too complex for this chapter, recognizing when an analysis should account for (or should have accounted for) clustering is relatively easy, and the number of clustering levels can get as complex as the researcher desires. For example, patients could be nested within a doctor, the doctor could be nested within a clinic, the clinic could be nested within a hospital system, the hospital system could be nested within a city, and so on. When reading a journal article, take time to consider whether clustering should have been considered by the researchers. Do not be disheartened by the possibility of seeing an exorbitant number of clustering levels in any journal article. If clustering is considered, most studies in the biomedical sciences will only use two or three clustering units. The takeaway message is that careful thought must be undertaken when evaluating a study, especially when considering clustering. If clustering levels were not considered, but should have been, interpret all results with caution. As an example of how to use Tables 8–3 and 8–4, say a researcher wanted to examine the effect a new statin medication had on the number of low-density lipoproteins (LDL) using a sample of 200 healthy patients. Although LDL has received a bad reputation, research has shown that the size of the LDL particles carrying the cholesterol is more predictive of future cardiovascular problems than the absolute LDL value measured in mg/dL.29 More specifically, cholesterol carried by large, buoyant LDLs (i.e., Pattern A) has little association with cardiovascular problems, whereas cholesterol carried by small, dense LDLs (i.e., Pattern B)

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has been associated with a myriad of cardiovascular problems.30 Therefore, a statin that only targets cholesterol carried by small, dense LDL is needed. Based on lipoprotein particle profile (LPP) testing, patients were placed into one of two groups based on LDL particle size (i.e., the IV: Pattern A versus Pattern B). The outcome for the study was LDL measured in mg/dL. Baseline measurements and demographic data were used as covariates and included categorized age (i.e., 40–49, 50–59, etc.), race, socioeconomic status indicated by whether the patient’s mother graduated from high school, comorbid conditions, concurrent medications, and baseline LDL. The researchers hypothesized that the statin should reduce LDL significantly more in Pattern A patients compared to Pattern B patients. The analysis was completed at the end of a 6-month study period. In the method section, the researchers stated that baseline characteristics and demographic data were compared between the two groups of patients. Due to the presence of outliers, Mann-Whitney tests were used for all continuous variables and chi-square tests were used for categorical variables. For the primary analysis, multiple linear regression was used. No assumption violations were indicated. The example above contains similar information to what is typically provided in the overwhelming majority of published literature. Thus, using the example information above, Tables 8–3 and 8–4 can be used to determine whether the statistical tests employed were appropriate. Note that all three of these tests are described in detail later in the chapter. For the baseline and demographic data, using Table 8–3, the Mann-Whitney test was used because the DV scale was continuous, distributional assumptions were not met due to outliers, the DV was only measured on one occasion, there was one IV with two levels, and no covariates were considered. Further, using Table 8–4, chi-square tests were used because both the DV and IV were categorical and no covariates were considered. For the primary analysis, using Table 8–4, multiple linear regression analysis was used because the scale of the DV was continuous, the distributional assumptions were met, there was one IV with two levels, covariates were considered measured on both categorical and continuous scales, and although the DV was measured twice, the baseline measurement was used only as an additional covariate. Based on this information, all three statistical tests were used appropriately.

Case Study 8–2 Tranexamic acid (TXA) and epsilon-aminocaproic acid (EACA) are two antifibrinolytics used to reduce blood loss following total joint arthroplasty. Because TXA is considerably more expensive than EACA, researchers at a local hospital currently dosing patients with TXA are interested in whether they can reduce costs by dosing EACA with no additional risk of blood loss. Therefore, a study was designed evaluate to differences in blood loss prevention between TXA and EACA following total joint arthroplasty. A sample of patients

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will be consented to participate in the study, with equal numbers randomized to receive either TXA or EACA. Blood loss will be measured using hemoglobin (Hbg). On the day of surgery, during the preoperative process, blood will be drawn to measure the patient’s baseline Hbg to serve as a covariate in analysis. Hbg will be measured again from blood drawn 2 days post-operatively to serve as the primary outcome. Hbg will be treated as a continuous variable for analysis. Because the local hospital only has three orthopedic surgeons who complete total hip and knee replacements, the decision was made to recruit orthopedic surgeons from other hospitals in the metropolitan area to participate in the study. Thirty additional orthopedic surgeons within five hospitals agreed to participate. Because surgeons often have privileges to perform surgery at multiple hospitals, they were asked to include surgeries at only one hospital of their choosing. Questions: 1. Would you consider this study to be a randomized controlled trial? 2. Is the design parallel-groups, crossover, or adaptive? Why? 3. The researchers plan to use a one-tailed hypothesis for this study. Is this appropriate? Could the researchers have specified a two-tailed hypothesis? 4. Are the researchers interested in a statistical test of differences or association? 5. How many times was the DV measured? 6. The distributional assumptions have been met and authors have indicated they will use an independent-samples t-test to analyze their data. Is this correct given the study design? If so, why? If not, indicate which statistical test(s) of group differences would be more appropriate. 7. The distributional assumptions have been met and the authors have indicated they will use some form of regression analysis to analyze their data. Is this appropriate? What type of regression is most appropriate? 8. Should the researchers have been concerned with clustering or nesting? Why or why not? If so, describe the levels of nesting within their study design.

Statistical Tests The remainder of this chapter describes the application and assumptions of numerous parametric and nonparametric statistical tests commonly used in the biomedical sciences. Note that this section only covers statistical tests applicable to study designs with one measured DV; no truly multivariate tests are discussed. A description and list of

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assumptions are provided for each statistical test as well as an example with an associated results section as it would likely appear in the literature. It is important to take careful note of the assumptions for each statistical test, as these assumptions are vital in determining whether correct statistical inference can be inferred from the statistical test results. The discussion of statistical tests begins with tests for nominal and categorical data, followed by statistical tests for evaluating group differences and associations.

TESTS FOR NOMINAL AND CATEGORICAL DATA Nonparametric Tests Pearson’s chi-square test Pearson’s chi-square test (or simply the chi-square test) is one of the most common statistical tests used in the biomedical sciences. It is used to assess for significant differences between two or more mutually exclusive groups for two variables measured on a nominal scale. Note that the data may also be ordinal, if the number of rank ordered categories is small; however, the test does not consider rank order. The chi-square test assesses for differences between actual or observed frequency counts and the frequency count that would be expected if there actually was no differences in the data. As an example, consider a study to determine whether a significant difference in gender exists between three treatment groups. In most journal articles, a 2 × 3 (i.e., gender by treatment group) contingency containing the observed frequency counts within each cell will typically be presented. This table would appear similar to Figure 8–7 but with another row or column. The expected frequencies are rarely presented in the literature. Next, a chi-square (χ2) statistic should be presented with appropriate degrees of freedom subscripted next to the chi-square symbol (e.g., χ22 for two degrees of freedom). As stated above, the number of degrees of freedom will vary based on the analysis. If the probability of the difference is below alpha; that is, if the observed frequencies are different from the expected frequencies, the test is considered statistically significant indicating a significant gender difference between the groups. The results of a statistically significant gender difference are presented as follows: The results of the chi-square test indicated a statistically significant gender difference between treatment groups (χ22 = 11.59, p < 0.05). When the chi-square test is based on a contingency table larger than 2 × 2, the test is considered an omnibus test. That is, in the example above for the 2 × 3 table the chisquare test indicated a statistically significant gender difference existed between at least two treatment groups, but failed to indicate specifically which treatment groups differed. In these situations, post hoc chi-square tests (or Fisher’s exact tests if expected frequencies are low, see the next section) are used to determine where statistically significant differences occurred. For example, in the 2 × 3 chi-square above, three 2 × 2

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post hoc chi-square tests are required. Specifically, gender compared between groups A and B, gender compared between groups A and C, and gender compared between groups B and C. A sample results section including the post hoc chi-square test results is presented below: Statistically significant gender differences were indicated across the three treatment groups (χ22 = 11.59, p < 0.05). Post hoc chi-square tests indicated statistically significant gender differences between groups A and B (χ21 = 6.54, p < 0.05) and between groups B and C (χ21 = 10.26, p < 0.05), with group B including significantly more males compared to both groups A and C. Further, no statistically significant gender difference was indicated between groups A and C. The assumptions of the chi-square test include: 1. Data for both variables being compared must be categorical. a. Note that continuous data can be categorized; however, information will be lost via categorization. 2. The categories must be mutually exclusive. a. That is, each individual can fall into one, and only one, category. 3. The total sample size must be large. a. The expected frequencies in each cell must not be too small. For chi-square tests with degrees of freedom greater than 1 (i.e., when the number of columns and/or rows are greater than 2), no more than 20% of the cells should have expected frequencies less than 5. Further, no cell should have an expected frequency less than 1.31 This is a difficult assumption to verify from the literature, outside of calculating the expected frequencies by hand. However, if an article fails to indicate this assumption was tested, view results with caution.

Fisher’s exact test Fisher’s exact test is ubiquitous in the biomedical literature. The test can only be applied to 2 × 2 contingency tables and is most useful when the sample size is small. It is often used when the third assumption of the chi-square test is violated. Conceptually, Fisher’s exact test is identical to the chi-square test, in that, the two variables being compared must be nominal and have mutually exclusive categories. For example, consider a study assessing for differences in cardiac events in dialysis patients who initiated beta blocker therapy compared to patients who did not initiate therapy. Note, both variables are dichotomous (i.e., event versus no event; beta blocker versus no beta blocker). Fisher’s exact test provides the exact probability of observing this particular set of frequencies within each cell of the contingency table. Results of a statistically significant Fisher’s exact test are presented below. Notice only a p value is provided:

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The results of a Fisher’s exact test indicated patients initiating beta blocker therapy had significantly fewer cardiac events compared to patients failing to initiate therapy (p < 0.05). The assumptions of Fisher’s exact test include: 1. Data for both variables being compared must be dichotomous. a. Note that continuous data can be dichotomized; however, information will be lost via categorization. 2. The dichotomous categories must be mutually exclusive. a. That is, each individual can fall into one, and only one, category.

Mantel-Haenszel chi-square test The Mantel-Haenszel chi-square test (also known as Cochran-Mantel-Haenszel test or Mantel-Haenszel test) measures the association of three discrete variables, which usually consists of two dichotomous IVs and one categorical confounding variable or covariate used as a stratification variable. For example, consider a study assessing the presence or absence of lung cancer in smokers and nonsmokers (the IVs) after stratifying for frequent exposure to secondhand smoke (the dichotomous covariate; exposure versus no exposure). A 2 × 2 contingency table is created at each level of secondhand smoke. That is, a contingency table for exposure and another for no exposure. This test produces a chi-square statistic (χ2MH), with a statistically significant result indicating a significant difference in the presence of lung cancer for smokers and nonsmokers across the levels of the covariate (i.e., exposed versus unexposed). A statistically significant result is presented as follows: The results of a Mantel-Haenszel chi-square test indicated the proportion of nonsmokers developing lung cancer was significantly greater for those exposed to second hand smoke (χ2MH = 29.67, 1 df, p < 0.05). The assumptions of the Mantel-Haenszel chi-square test include: 1. Data of the IVs must be dichotomous. a. Note that continuous data can be dichotomized; however, information will be lost via categorization. 2. The dichotomous categories must be mutually exclusive. a. That is, each individual can fall into one, and only one, category. 3. Data of the covariate must be categorical. a. Again, note that continuous data can be dichotomized; however, information will be lost via categorization.

The kappa statistic The kappa statistic (also known as Cohen’s kappa or κ) is a measure of inter-rater reliability or agreement for a categorical variable measured on a nominal scale. That is,

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kappa indicates how often individual raters using the same measurement scale indicate identical scores. Kappa provides the proportion of agreement corrected for chance and ranges from 0 indicating no agreement to 1 indicating perfect agreement. Be aware that there are no methodological studies defining a threshold for what could be considered good agreement. The definition of good agreement varies by substantive area. That is, what is considered good agreement in medical literature may not be considered good agreement in psychology, or vice versa. In general, however, a higher kappa is always better. Because this statistic corrects for chance agreement, it is more appropriate than simply calculating overall percent agreement.32 In fact, percent agreement should rarely be used and published results using percent agreement should be viewed with caution. Kappa can be applied to a variable with any number of categories, with the understanding that as the number of categories increase, overall agreement will undoubtedly decrease. That is, the more choices two raters have, the less likely they are to agree. As an example, consider 100 professional school applicants, who each interview with two faculty members. After the interview is complete, each faculty member rates the applicant as accept, deny, or waitlist. Kappa is then used to calculate the agreement between faculty members. Results using the kappa statistic are presented as follows: Cohen’s kappa was employed to measure the agreement between faculty members in determining whether applicants should be accepted, denied, or waitlisted. Results indicated moderate agreement between faculty members (κ = 0.75). The assumptions of the kappa statistic include: 1. Each object (e.g., the applicant in the example above) is rated only one time. 2. The outcome variable is nominal with mutually exclusive categories. a. That is, each individual can fall into one, and only one, category. 3. There are at least two independent raters. a. That is, each rater provides one, and only one, response for each applicant.

TESTING FOR DIFFERENCES FROM THE POPULATION Parametric Tests One-sample z test The one-sample z test is used to assess for a difference between the mean of the study sample and a known population mean using a continuous DV. For example, consider data collected from a random sample of 1000 patients with borderline high cholesterol, for which their mean serum total cholesterol was 210.01 mg/dL. The researcher is interested in determining whether the total cholesterol of this sample is significantly higher than the mean total cholesterol within the general population.

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The 2007-2008 National Health and Nutrition Examination Survey (NHANES) determined the mean serum total cholesterol level for individuals in the United States aged 6 years and older is 186.67 mg/dL with a standard deviation of 42.15.33 A one-sample z test provides a z-score indicating how many standard errors the sample mean is from the known population mean and if this difference is large enough to be considered statistically significant based on specific degrees of freedom. Note that degrees of freedom will be subscripted next to the z-score (e.g., z999). Results of a statistically significant one-sample z test with no assumption violations are provided as follows: Results of a one-sample z test indicated a statistically significant difference in total cholesterol between the study sample and population (z999 = 2.10, p < 0.05), with the study sample having significantly higher total cholesterol compared to the general population (210.01 mg/dL versus 186.67 mg/dL, respectively). Assumptions of the one-sample z test include: 1. The DV is measured on an interval or ratio scale. 2. The sampling distribution of means for the DV is normal. a. This can be ensured by applying the central limit theorem. 3. The population mean and standard deviation is known. 4. The observations are independent. a. That is, each participant provides one, and only one, observation (i.e., data or response).

One-sample t-test Only in rare cases is the population standard deviation known; thus, test statistics often must be based on sample data (i.e., standard deviation and sample size). The one-sample t-test is used in situations where only the population mean is known, or can at least be estimated by very large amounts of data. It is used only for a continuous DV. For example, consider a study to compare the mean total cholesterol of a random sample of 1000 adults aged 20 years of age or older with borderline high cholesterol (e.g., 231.26 mg/dL) to the mean total cholesterol of the general population. In 2006, the National Center for Health Statistics determined the mean serum total cholesterol for adults in the United States aged 20 years and older was 199.00 mg/dL.34 Notice, no population standard deviation is available; thus, a one-sample t-test is required. Note that this test produces a t statistic, which can be considered similar to a z-score when samples are large. In general, a t-test will approximate a z-test with a sample size of around 30. The result of a statistically significant one-sample t-test with no assumption violations is presented as follows: Results of a one-sample t-test indicated a statistically significant difference in total cholesterol between the study sample and population (t999 = 2.23, p < 0.05), with the study

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sample having significantly higher total cholesterol compared to individuals aged 20 or older in the general population (231.26 mg/dL versus 199.00 mg/dL, respectively). Assumptions of the one-sample t-test include: 1. The DV is measured on an interval or ratio scale. 2. The sampling distribution of means for the DV is normal. a. This can be ensured by applying the central limit theorem. 3. The population mean is known. 4. The observations are independent. a. That is, each participant provides one, and only one, observation (i.e., data or response).

Nonparametric Tests Binomial test The binomial test is used when the DV is dichotomous and all of the possible data or outcomes fall into one, and only one, of the two categories. The binomial test uses the binomial distribution to test the exact probability of whether the sample proportion differs from the population proportion. Further, the binomial test is often used in the literature when sample sizes are small and violate the assumptions of the chi-square test, specifically low expected frequencies.27 For example, say a fair coin is flipped 10 times, and lands on heads 6 of the 10 flips. The expected population proportion is 0.50; that is, if the coin is fair, as the number of flips increases the coin should land on heads 50% of the time. Because the coin landed on heads 6 of the 10 flips, the statistical test is whether this proportion (i.e., 6/10 or 0.60) is statistically different from the expected proportion (i.e., 0.50). In this case, the binomial test indicates the difference between these proportions is not significant and results are presented as follows: Results of the binomial test indicated the probability of flipping 6 heads in 10 flips was not statistically different from the expected population proportion of 0.50 (p > 0.05). The assumptions of the binomial test include: 1. Data for both variables being compared must be dichotomous. a. Note that continuous data can be dichotomized; however, information will be lost via categorization. 2. The dichotomous categories must be mutually exclusive. a. That is, each individual can fall into one, and only one, category. 3. The population proportion is known. 4. The observations are independent. a. That is, each participant provides one, and only one, observation (i.e., data or response).

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Kolmogorov-Smirnov one-sample test The Kolmogorov-Smirnov one-sample test is a goodness-of-fit test used to determine the degree of agreement between the distribution of a researcher’s sample data and a theoretical population distribution.27 That is, it allows researchers to compare the distribution of their sample data against a given probability distribution for a continuous DV (see Table 8–1). For example, consider a study where HbA1c data was collected for a random sample of 100 patients with diabetes. The researcher is interested in determining whether the distribution of HbA1c data was sampled from a population of patients with an underlying normal distribution. That is, the researcher is interested in whether the sample data is normally distributed. A nonsignificant Kolmogorov-Smirnov test indicates the sample distribution and the hypothesized normal distribution are not statistically different; that is, the distribution of sample data can be considered normally distributed. Results of the Kolmogorov-Smirnov test are presented as follows: Results of the Kolmogorov-Smirnov test indicated HbA1c variable had a nonsignificant departure from normality (p > 0.05); thus, the data are considered to result from a normal distribution. The assumptions of the Kolmogorov-Smirnov one-sample test include: 1. The DV is measured on an interval or ratio scale. 2. The underlying population distribution is theorized or known. a. That is, the researcher must specify the correct probability distribution to test the sample data against. If the distribution is unknown, the test is inappropriate. 3. The observations are independent. a. That is, each participant provides one, and only one, observation (i.e., data or response).

TESTING FOR DIFFERENCES BETWEEN GROUPS Parametric Tests Independent-samples t-test The independent-samples t-test (also known as Student’s t-test) is used to assess for a statistically significant difference between the means of two independent, mutually exclusive groups using a continuous DV. For example, consider testing for a mean difference in a methacholine challenge at the end of an 8-week study period in two groups of asthma patients receiving either rosiglitazone or placebo. Methacholine challenge was measured by a 20% decrease in forced expiratory volume in one second (FEV1; PC20). The independent-sample t-test provides the t statistic and probability of obtaining a difference of this size or larger based on

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specific degrees of freedom, which are usually subscripted (e.g., t31). Results of a statistically significant independent-samples t-test with no assumption violations are presented as follows: The results of an independent-samples t-test indicated a statistically significant difference between groups (t31 = 9.654, p < 0.05), with asthma patients receiving rosiglitazone displaying significantly better lung function compared to placebo (mean PC20 = 10.7 mg/mL versus 3.8 mg/mL, respectively). The assumptions of the independent-samples t-test include: 1. The DV is measured on an interval or ratio scale. 2. The sampling distribution of means for the DV within each level of the IV (i.e., group) is normal. a. This can be ensured by applying the central limit theorem. 3. The IV is dichotomous. a. Note that continuous data can be dichotomized; however, information will be lost via categorization. 4. The IV categories are mutually exclusive. a. That is, each individual can fall into one, and only one, category. 5. Homogeneity of variance is ensured. a. That is, the variance within each group is similar. A crude indicator of a violation of this assumption (i.e., heterogeneity) is the ratio of the largest variance to smallest variance being greater than 10:1.22 For example, most studies do not provide the variance for each variable; however, the standard deviation is reported consistently. Remember, variance is simply the standard deviation squared. Thus, consider two variables with standard deviations of 5 and 10. The homogeneity of variance assumption can be tested by squaring the standard deviations (i.e., 52 = 25 and 102 = 100, respectively) and finding their ratio (i.e., 100/25 = 4). In this case, the ratio is less than 10:1; thus, the assumption is not violated. 6. The observations are independent. a. That is, each participant provides one, and only one, observation (i.e., data or response).

One-way between-groups analysis of variance A one-way between-groups analysis of variance (ANOVA) is an extension of the independent-samples t-test to situations, where researchers want to assess for mean differences between three or more mutually exclusive groups using a continuous DV. For example, consider the rosiglitazone example from the Independent-Samples t-Test section above, but in addition to the placebo group, include two groups receiving different doses of rosiglitazone (e.g., 4 and 8 mg). The use of three independent-samples

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t-tests to test for mean differences between groups (i.e., 4 mg versus placebo, 8 mg versus placebo, 4 mg versus 8 mg) is inappropriate due to a possible increase in Type I error. Instead, one-way ANOVA is used to partition the variance between and within groups to determine if a statistically significant group difference exists. This partitioning can be observed in the two numbers presented for degrees of freedom (i.e., F2,27 indicates 2 between-group degrees of freedom and 27 within-group degrees of freedom). The result of a statistically significant one-way ANOVA with no assumption violations is presented as follows: Results of a one-way ANOVA indicated a statistically significant difference between groups (F2,27 = 6.89, p < 0.05). ANOVA provides an omnibus F test; that is, an overall test assessing the statistical significance between the three or more group means. A statistically significant omnibus F test indicates a statistically significant difference between at least two group means. To determine which groups differ specifically, a series of post hoc tests are conducted. Post hoc tests are simply tests comparing individual groups to one another; thus, post hoc tests can be viewed as a series of independent-samples t-tests. That is, two-group comparisons. Because post hoc tests increase the number of statistical tests used, they often use a more conservative alpha to control for potential Type I errors. With this in mind, the significant one-way ANOVA in the example above would require three adjusted post hoc tests. That is, 4 mg versus placebo, 8 mg versus placebo, and 4 mg versus 8 mg. The most commonly used post hoc tests in the literature include the Tukey and Scheffé tests. Be aware that the Scheffé test is the most conservative post hoc test available and some methodologists argue that the test may be too conservative increasing the probability of committing a Type II error. A suitable alternative is the Tukey test, which is conservative but to a lesser degree. In most cases, the two tests will indicate similar results and both are viewed as acceptable. The results of a statistically significant one-way ANOVA including post hoc tests are presented as follows: Results of a one-way ANOVA indicated a statistically significant difference between groups (F2,27 = 6.89, p < 0.05). Post hoc Tukey tests indicated statistically significant differences (at p < 0.05) between placebo (3.8 mg/mL) and 4 mg dose of rosiglitazone (10.7 mg/mL) as well as between placebo and the 8 mg dose of rosiglitazone (12.2 mg/mL). No statistically significant differences were indicated between the 4 mg and 8 mg doses of rosiglitazone. The assumptions for one-way ANOVA include: 1. The DV is measured on an interval or ratio scale. 2. The sampling distribution of means for the DV within each level of the IV is normal. a. This can be ensured by applying the central limit theorem.

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3. The levels of the IV are mutually exclusive. a. That is, each individual can fall into one, and only one, category. 4. Homogeneity of variance is ensured. a. That is, the variance within each group is similar. A crude indicator of a violation of this assumption (i.e., heterogeneity) is the ratio of the largest variance to smallest variance being greater than 10:1.22 For example, most studies do not provide the variance for each variable; however, the standard deviation is reported consistently. Remember, variance is simply the standard deviation squared. Thus, consider two variables with standard deviations of 5 and 10. The homogeneity of variance assumption can be tested by squaring the standard deviations (i.e., 52 = 25 and 102 = 100, respectively) and finding their ratio (i.e., 100/25 = 4). In this case, the ratio is less than 10:1; thus, the assumption is not violated. 5. The observations are independent. a. That is, each participant provides one, and only one, observation (i.e., data or response).

Factorial between-groups analysis of variance A factorial between-groups analysis of variance (also known as factorial ANOVA) is an extension of the one-way between-groups ANOVA to a study with more than one IV using a continuous DV. For example, consider a study to evaluate for differences in heart rate measured by beats per minute (bpm) between men and women following either a 25-mg dose of pseudoephedrine or placebo. In the literature, this may be described as a 2 × 2 factorial design indicating two IVs (i.e., gender and treatment) each with two levels (i.e., male versus female; pseudoephedrine versus placebo). This type of design produces two main effects—one for gender and one for treatment—and an interaction effect between gender and treatment. Thus, three separate F tests are provided, one for each effect, with statistical significance determined separately for each effect. It is extremely important to note that if the interaction effect is statistically significant, the results of the main effects cannot be interpreted directly, as the IVs are dependent on each other. From the example, a statistically significant interaction effect indicates treatment effects differ depending on the gender of the participant. That is, pseudoephedrine had a different effect for males than it did for females. However, if the interaction effect is not significant, main effects can and should be interpreted. When interpreting the main effect of an IV, the levels of the other IV are averaged or marginalized. That is, interpreting the main effect of gender is done irrespective of whether the participants received pseudoephedrine or placebo. Likewise, interpreting the main effect of treatment is done irrespective of the participant’s gender.

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Similar to one-way between-groups ANOVA, following a statistically significant main effect or interaction, post hoc tests may be required to identify where statistically significant differences occurred. There are a number of post hoc tests available depending on whether the interaction or main effects are statistically significant including the Tukey and Scheffé tests.35 Each post hoc test adjusts alpha more or less conservatively to reduce potential Type I errors. Post hoc tests for factorial ANOVA used in the literature are often termed simple comparisons, simple contrasts, simple main effects, or interaction contrasts. While each uses a slightly different procedure, they are used to accomplish the same goal—identify group differences. In the biomedical sciences, the results of a nonsignificant interaction effect for a 2 × 2 factorial between-groups ANOVA with no assumption violations are presented as follows: Results of a 2 (gender; male versus female) × 2 (treatment; pseudoephedrine versus placebo) factorial between-groups ANOVA indicated a nonsignificant interaction effect between gender and treatment (p > 0.05). However, the main effect for gender was statistically significant (F1,26 = 21.36, p < 0.05), with males having significantly higher heart rates than females (91.6 bpm versus 84.3 bpm, respectively). Further, the main effect of treatment was also statistically significant (F1,26 = 15.24, p < 0.05), with pseudoephedrine resulting in a significantly higher heart rate compared to placebo (70.3 bpm versus 65.2 bpm, respectively). The results of a 2 × 2 factorial between-groups ANOVA with a statistically significant interaction and no assumption violations are presented as follows: Results of a 2 (gender; male versus female) × 2 (treatment; pseudoephedrine versus placebo) factorial between-groups ANOVA indicated a statistically significant interaction effect between gender and treatment (F1,26 = 15.42, p < 0.05). Simple main effects were assessed to identify at which treatment level gender differed. Results indicated pseudoephedrine increased heart rate significantly higher for males compared to females (90.5 bpm versus 82.4 bpm, respectively). No statistically significant gender difference in heart rate was indicated for the placebo group. The assumptions of factorial between-groups ANOVA include: 1. The DV is measured on an interval or ratio scale. 2. The sampling distribution of means for the DV within each level of the IV is normal. a. This can be ensured by applying the central limit theorem. 3. The levels of the IVs are mutually exclusive. a. That is, each individual can fall into one, and only one, category. 4. Homogeneity of variance is ensured. a. That is, the variance within each group is similar. A crude indicator of a violation of this assumption (i.e., heterogeneity) is the ratio of the largest variance to smallest variance being greater than 10:1.22 For example, most studies do not provide

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the variance for each variable; however, the standard deviation is reported consistently. Remember, variance is simply the standard deviation squared. Thus, consider two variables with standard deviations of 5 and 10. The homogeneity of variance assumption can be tested by squaring the standard deviations (i.e., 52 = 25 and 102 = 100, respectively) and finding their ratio (i.e., 100/25 = 4). In this case, the ratio is less than 10:1; thus, the assumption is not violated. 5. The observations are independent. a. That is, each participant provides one, and only one, observation (i.e., data or response).

Analysis of covariance Analysis of covariance (ANCOVA) is an extension of both one-way between-groups ANOVA and factorial between-groups ANOVA. ANCOVA evaluates main effects and interactions using a continuous DV after statistically adjusting for one or more continuous confounding variables. That is, ANCOVA adjusts all group means to create the situation as if all participants scored identically on the covariate.22 For example, consider a study comparing atenolol to placebo (IV) and assessing their effects on systolic blood pressure (DV). The researchers note, however, that previous research has shown systolic blood pressure and BMI to be highly correlated.36 Thus, the analysis will include BMI as a covariate assessing the effect of atenolol on systolic blood pressure over and above the effect of BMI on systolic blood pressure. If the atenolol group has greater BMI values compared to the placebo group, ANCOVA will adjust the systolic blood pressure within both groups to account for this initial difference in BMI. In ANCOVA, covariates are continuous, measured before the DV, and correlated with the DV. It should be noted that ANCOVA is closely related to linear regression. Thus, although not completely necessary, it may be useful to revisit this section after reading the sections on Simple and Multivariable Linear Regression presented later in the chapter. In ANCOVA, group means are statistically adjusted by the magnitude of the association (i.e., slope) between the DV and covariate.37 That is, the greater the association, the more useful the covariate and better the adjustment. Thus, the goal of the covariate is to reduce error variance thereby increasing the statistical power of the test. From the example, mean systolic blood pressure for the atenolol and placebo groups are adjusted by the association between BMI and systolic blood pressure. Because previous research has shown the association between systolic blood pressure and BMI to be considerable, the statistical power of this test will undoubtedly be increased. When presenting the results of ANCOVA, researchers should provide adjusted means; that is, the mean of the DV at each level of the IV after adjusting for the covariate. Published research that does not present adjusted means should be viewed with caution. Further, the effect of the covariate must also be presented which provides information

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regarding the effectiveness of the covariate in adjusting group means. Finally, it should be noted that ANCOVA is more suited for experimental design in which participants are randomized to groups, as opposed to nonexperimental designs without randomization. Remember, ANCOVA is used to adjust group means as if all participants had identical covariate values. However, in nonexperimental research, important covariates may have been missed and causality is difficult to infer—a characteristic intrinsic to all nonexperimental work. Thus, the limitations may be significant when applying ANCOVA to nonexperimental designs and results must be viewed cautiously.9 In the biomedical sciences, the results of a statistically significant ANCOVA with no assumption violations are presented as follows: Results of a one-way ANCOVA indicated a statistically significant group differences in systolic blood pressure after adjusting for BMI (F1,17 = 7.98, p < 0.05), with patients receiving atenolol having significantly lower systolic blood pressure compared to placebo (adjusted means = 118 mm Hg versus 141 mm Hg, respectively). The relationship between systolic blood pressure and BMI was also statistically significant after adjusting for group (F1,17 = 39.85, p < 0.05) with a pooled within-group correlation of 0.61. The assumptions of ANCOVA include: 1. The DV is measured on an interval or ratio scale. 2. The sampling distribution of means for the DV and covariate(s) within each level of the IV is normal. a. This can be ensured by applying the central limit theorem. 3. The levels of the IV are mutually exclusive. a. That is, each individual can fall into one, and only one, category. 4. Homogeneity of variance is ensured. a. That is, the variance within each group is similar. A crude indicator of a violation of this assumption (i.e., heterogeneity) is the ratio of the largest variance to smallest variance being greater than 10:1.22 For example, most studies do not provide the variance for each variable; however, the standard deviation is reported consistently. Remember, variance is simply the standard deviation squared. Thus, consider two variables with standard deviations of 5 and 10. The homogeneity of variance assumption can be tested by squaring the standard deviations (i.e., 52 = 25 and 102 = 100, respectively) and finding their ratio (i.e., 100/25 = 4). In this case, the ratio is less than 10:1; thus, the assumption is not violated. 5. Homogeneity of regression is ensured. a. This assumption requires the association between the DV and covariate to be the same within each level of the IV. A violation of this assumption renders ANCOVA inappropriate, and the authors should use linear regression instead. However, violation is difficult to detect from the literature, as most authors fail

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to provide the appropriate information in the narrative. Thus, when reading a journal article employing ANCOVA, if the author fails to indicate whether this assumption was tested, results must be viewed with caution. 6. The covariate(s) is measured reliably and without error. 7. The observations are independent. a. That is, each participant provides one, and only one, observation (i.e., data or response).

Nonparametric Tests Mann-Whitney test The Mann-Whitney test is the nonparametric alternative to the independent-samples t-test and is one of the most powerful nonparametric tests.27 It is used when the distributional assumptions for the parametric test are violated or when the DV is measured on an ordinal scale. The Mann-Whitney test is based on ranked data. That is, instead of using the actual values of the DV, as an independent-samples t-test does, each participant’s DV value is ranked with the highest value receiving the highest rank and the lowest value receiving the lowest rank. The ranks within each group are then summed and the test assesses whether the difference in ranked sums between groups is statistically significant. For example, consider a performance improvement study assessing gender differences in patient satisfaction of hospital stay following total hip replacement surgery. The measurement instrument uses a Likert-type scale with four possible responses anchored from Strongly Disagree to Strongly Agree. A statistically significant Mann-Whitney test indicates gender differences in patient satisfaction, with the group with the highest ranked sums indicating higher satisfaction. The results of the Mann-Whitney test are presented as follows: The results of a Mann-Whitney test indicate a statistically significant gender difference in patient satisfaction following total hip replacement surgery (z = 2.65, p < 0.05), with males indicating higher satisfaction scores compared to females. The assumptions of the Mann-Whitney test include: 1. The DV is measured on an ordinal, interval, or ratio scale. 2. The IV is dichotomous. a. Note that continuous data can be categorized into a dichotomous variable; however, information will be lost. 3. The levels of the IV are mutually exclusive. a. That is, each individual can fall into one, and only one category. 4. The observations are independent. a. That is, each participant provides one, and only one, observation (i.e., data or response).

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Median test The median test is used to assess whether two mutually exclusive groups have different medians. There is no parametric alternative to the median test; however, the nonparametric Mann-Whitney test can be used as an adequate alternative. The test calculates the medians within each group and then classifies the data within each group as either above or below the respective group median. Further, because the test is based on the median, it can be used appropriately for skewed distributions or data containing outliers. For example, consider a study evaluating gender differences in childhood autism as measured by the Childhood Autism Spectrum Test (CAST).37 The CAST measures difficulties and preferences in social and communication skills using the total score a 37-item questionnaire, with lower scores indicating fewer symptoms. Because autism is a relatively rare disorder, the distribution of CAST scores is expected to have severe positive skewness due to outliers. That is, most children will score low, while a few autistic children will have high scores. The median test was used to determine whether statistically significant gender differences existed in CAST scores. The results of a statistically significant median test are presented as follows: The results of the median test indicated gender differences in CAST scores (p < 0.05), with boys having a significantly higher median score compared to girls (median = 5 versus median = 4, respectively). Assumptions of the median test include: 1. The DV is measured on an ordinal, interval, or ratio scale. 2. Samples sizes are sufficiently large. a. If sample sizes are small, say less than 5 in each group, Fisher’s exact test should be used instead. From the example, this means using a 2 (Group; male versus female) × 2 (Median; above versus below) contingency table. 3. The observations are independent. a. That is, each participant provides one, and only one, observation (i.e., data or response).

Kruskal-Wallis one-way ANOVA by ranks The Kruskal-Wallis one-way ANOVA by ranks (or simply, the Kruskal-Wallis test) is the nonparametric alternative to the one-way between-groups ANOVA. The test is an extension of the Mann-Whitney test to assess group differences between three or more mutually exclusive groups. The Kruskal-Wallis test is typically used when distributional assumptions are violated or when the DV is measured on an ordinal scale. Further, the Kruskal-Wallis test is based on rank sums similar to the Mann-Whitney test. The DV scores are ranked from highest to lowest, summed, and statistically significant of group differences are evaluated based on these rank sums.

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For example, consider a study evaluating regional differences in whether volunteer preceptors believe they have adequate time available to dedicate to their experiential pharmacy students.38 In this study, the DV was measured on an ordinal 4-point Likert-type scale; thus, the Kruskal-Wallis test was used in lieu of one-way between-groups ANOVA. The results of the statistically significant Kruskal-Wallis test are presented as follows: Results of the Kruskal-Wallis test indicated regional differences regarding whether volunteer preceptors believe they have adequate time to dedicate to experiential students (χ26 = 33.07, p < 0.05). Similar to a one-way between-groups ANOVA, the Kruskal-Wallis test is an omnibus test. That is, the test will determine whether an overall statistically significant difference exists between groups, but will not indicate specifically which groups differed statistically. Thus, post hoc tests are required. In this situation, the Mann-Whitney test is used to compare all two-group combinations. From the example, post hoc tests would include West versus Midwest, West versus South, West versus Northeast, and so on for a total of six post hoc tests. The results of the Kruskal-Wallis test including Mann-Whitney post hoc tests are presented as follows: Results of the Kruskal-Wallis test indicated regional differences regarding whether volunteer preceptors believe they have adequate time to dedicate to experiential students (χ62 = 33.07, p < 0.05). Post hoc Mann-Whitney tests indicated preceptors in the West disagreed more compared to preceptors located in the Midwest (p < 0.05) and agreed less with preceptors in the South (p < 0.05). No other statistically significant group differences were indicated. The assumptions of the Kruskal-Wallis test include: 1. The DV is measured on an ordinal, interval, or ratio scale. 2. The IV is categorical. a. Note that continuous data can be categorized; however, information will be lost. 3. The levels of the IV are mutually exclusive. a. That is, each individual can fall into one, and only one category. 4. Each group has approximately the same distribution. a. Although the Kruskal-Wallis test does not assume data are distributed normally, if the distribution for one level of the IV is skewed negatively and the other levels are skewed positively, the results produced by the test may be inaccurate. 5. The data do not include a large number of ties. a. Tied values are given average ranks. Typically, if less than 25% of the data are ties, the test is unaffected.27 6. The observations are independent. a. That is, each participant provides one, and only one, observation (i.e., data or response).

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TESTING FOR WITHINGROUP CHANGE Parametric Tests Paired-samples t-test The paired-samples t-test (also known as matched t-test or nested t-test) is used when one group of participants in measured twice or two groups of participants are matched on specific characteristics. In both cases, the assumption of independence, or mutually exclusive groups, is violated. This statistical test is only appropriate using a continuous DV. When one group of participants is measured twice, it is known as a repeated measures design. Repeatedly measuring participants is a valid method for reducing error and increasing statistical power, which requires fewer participants. The simplest repeated measures design is termed a pretest-posttest design. For example, consider measuring the therapeutic knowledge of 20 fourth-year pharmacy (P4) students prior to clinical rotations (i.e., pretest) and following rotations (i.e., posttest) to assess for increases in therapeutic knowledge. Therapeutic knowledge was measured using a discriminating 20-question test. The paired-samples t-test assesses for a statistically significant change in correct responses from pretest to posttest. When two groups of participants are matched on specific characteristics, it is called a matched design. For example, when studying the effects of a new statin medication on hyperlipidemia, researchers would identify a group of patients to receive the statin and then identify a matched control group by matching individuals based on age, race, gender, BMI, and years with diagnosis. Note that the matched control group does not receive any medication. Matching participants serves the same purpose as repeated measures— reduce error variance—but is often more difficult because as the number of matching criteria increases the probability of finding a suitable match decreases. Results of a statistically significant paired-samples t-test with no assumption violations using the pretest-posttest design example above is presented as follows: The results of a paired-samples t-test indicated a statistically significant difference in therapeutic knowledge between pretest and posttest scores (t19 = 3.25, p < 0.05). Therapeutic knowledge increased significantly following clinical rotations (mean = 10.4 correct responses at pretest versus a mean of 16.5 at posttest). The assumptions of the paired-samples t-test include: 1. The DV is measured on an interval or ratio scale. 2. The two DV measurements are associated. 3. The sampling distribution of means for both DV measurements is normal. a. This can be ensured by applying the central limit theorem. 4. Homogeneity of variance for both DV measurements is ensured. a. That is, the variance within each group is similar. A crude indicator of a violation of this assumption (i.e., heterogeneity) is the ratio of the largest variance to

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smallest variance being greater than 10:1.22 For example, most studies do not provide the variance for each variable; however, the standard deviation is reported consistently. Remember, variance is simply the standard deviation squared. Thus, consider two variables with standard deviations of 5 and 10. The homogeneity of variance assumption can be tested by squaring the standard deviations (i.e., 52 = 25 and 102 = 100, respectively) and finding their ratio (i.e., 100/25 = 4). In this case, the ratio is less than 10:1; thus, the assumption is not violated.

One-way repeated measures analysis of variance A one-way repeated measures ANOVA (aka, repeated measures ANOVA) is an extension of the paired-samples t-test to situations where the continuous DV is measured three or more times. Again, this can occur when the same participants are measured repeatedly or when three or more matched groups are measured once. A repeated measures ANOVA is used to indicate whether statistically significant change occurred between the repeated measurements. For example, reconsider the pretest-posttest design described in the Paired-Samples t-Test section early in the chapter. Briefly, a researcher is interested in testing whether therapeutic knowledge of 20 pharmacy students in their last year of college changes before and after clinical rotations. To be applicable to repeated measures ANOVA, students would be tested on a third occasion 6 months after posttest to assess knowledge retention. That is, the design measures therapeutic knowledge at pretest, posttest, and 6-month follow-up. The repeated measures ANOVA is then used to test whether a statistically significance change occurred between the repeated measurements. It should be noted that some researchers prefer to use repeated measures ANOVA over paired-samples t-tests when participants are only measured twice. This is an appropriate use of repeated measures ANOVA as repeated measures ANOVA can be used in any situation when a paired-samples t-test is appropriate. The results would be identical. However, the test statistic from the repeated measures ANOVA will be an F value instead of a t value produced by the paired-samples t-test. This is a nonissue, though, as the F value in this situation is simply t2. In most cases, repeated measures ANOVA has more statistical power than a pairedsamples t-test. This has been alluded to in the Analysis of Clinical Trials and PairedSamples t-test sections earlier in the chapter. In general, increasing the number of repeated measures further reduces error, which allows for more precise measurement and decreases the overall probability of committing a Type I error. With that said, increasing the number of repeated measurements has diminishing returns in statistical power. That is, for most studies, statistical power will increase drastically by adding a few additional repeated measurements, but the magnitude of this increase weakens rapidly between four and six measurements, with little to no increases in statistical power beyond

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the seventh measurement.39 Finally, if a study has more than 10 repeated measurements, a time series analysis may be more appropriate than repeated measures ANOVA. In addition, a brief discussion of the key assumption of repeated measures ANOVA is useful as a basic understanding this assumption will assist in determining whether the test statistics produced from the analysis are correct. This key assumption, known as sphericity, states that the variances of the differences between repeated measurements are equal. For example, consider a study with three repeated measurements. For the sphericity assumption to be satisfied, the variance of the difference between the first and second measurements must be similar to the variance of the difference between the first and third and second and third. This assumption tends to be restrictive, as most differences closer in time tend to have less variability compared to measurements further apart in time. Sphericity is a testable assumption using Mauchly’s test, and a violation can severely bias the statistical inference. Therefore, when reading a journal article, if the authors fail to provide information regarding the assurance or violation of the sphericity assumption, results and interpretations must be viewed with caution. Briefly reconsider the example provided above, where 20 pharmacy students in their final year have therapeutic knowledge measured before clinical rotations (i.e., pretest), once immediately after rotations (i.e., posttest), and at a 6-month follow-up. That is, therapeutic knowledge is measured on three separate occasions. A statistically significant repeated measures ANOVA with no assumption violations will be presented as follows: The results of a one-way repeated measures ANOVA indicated a statistically significant difference in therapeutic knowledge between pretest, posttest, and 6-month follow-up (F2,38 = 9.87, p < 0.05). With more than two repeated measurements, the one-way repeated measure ANOVA is an omnibus test. That is, the F test will identify whether a statistically significant difference exists between repeated measures, but will not indicate specifically which repeated measurements differ. Thus, post hoc tests, known as pairwise comparisons, are required. Similar to other analysis requiring post hoc tests, there are numerous adjusted pairwise comparisons available. Each type of pairwise comparison adjusts alpha differently, with some being more conservative. It may be simpler to think of these comparisons as a series of paired-samples t-tests with adjusted alpha values. That is, adjusted pairedsamples t-tests comparing the first and second repeated measurements, the first and third, the second and third, and so on. The additional information required to present results of a statistically significant one-way repeated measures ANOVA with no assumption violations are presented as follows: The results of a one-way repeated measures ANOVA indicated a statistically significant difference in therapeutic knowledge between pretest, posttest, and 6-month follow-up (F2,38 = 9.87, p < 0.05). Results of the pairwise comparisons indicated a statistically significant increase in therapeutic knowledge from pretest to posttest (mean = 5.50 versus 15.90,

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respectively, p < 0.05). Further, no statistically significant difference was indicated from posttest to 6-month follow-up (mean = 15.90 versus 15.50, respectively) indicating therapeutic knowledge was retained for 6 months following clinical rotations. The assumptions of the one-way repeated measures ANOVA include: 1. The DV is measured on an interval or ratio scale. 2. All DV measurements are associated. 3. The sampling distribution of means for all DV measurements is normal. a. This can be ensured by applying the central limit theorem. 4. Homogeneity of variance for all DV measurements is ensured. a. That is, the variance within each group is similar. A crude indicator of a violation of this assumption (i.e., heterogeneity) is the ratio of the largest variance to smallest variance being greater than 10:1.22 For example, most studies do not provide the variance for each variable; however, the standard deviation is reported consistently. Remember, variance is simply the standard deviation squared. Thus, consider two variables with standard deviations of 5 and 10. The homogeneity of variance assumption can be tested by squaring the standard deviations (i.e., 52 = 25 and 102 = 100, respectively) and finding their ratio (i.e., 100/25 = 4). In this case, the ratio is less than 10:1; thus, the assumption is not violated. 5. Sphericity is ensured for designs with three or more repeated measurements. a. This is a complex assumption discussed above. In general, sphericity is violated when the variance of the differences between measurements are not similar.

Mixed between-within analysis of variance A mixed between-within analysis of variance (also known as factorial ANOVA with repeated measures or split-plot ANOVA) is a combination of factorial between-groups ANOVA and repeated measures ANOVA. The mixed terminology highlights this combination, and indicates that the design considers two or more levels of the IV when a continuous DV is measured repeatedly. It is important to note that a mixed between-within ANOVA is qualitatively different from a mixed-effects analysis involving random effects (see Table 8–4). The simplest case is a 2 × 2 pretest-posttest design, using two mutually exclusive treatment groups measured on two separate occasions. The primary advantage of this analysis is that it allows researchers to assess the interaction effect evaluating whether two groups changed differently over time in addition to between-subjects main effect indicating the overall effect irrespective of measurement and within-subjects main effect indicating the overall effect irrespective of group. As an example, consider a study examining the effectiveness of a relatively new FDAapproved tricyclic antidepressant (TCA) compared to amitriptyline over a 12-week study period. The researcher hypothesizes that the new TCA is more effective than amitriptyline

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in reducing symptoms of clinical depression. Prior to initiating treatment, 20 patients with diagnosed clinical depression are measured on the Beck Depression Inventory II (BDI-II).40 Following this pretest or baseline measurement, each patient is randomized to receive one of two treatment options, the new TCA or amitriptyline, with 10 patients in each group. Patients then initiate the prescribed medication therapy and at the end of the 12-week study period BDI-II scores are measured again. A mixed between-within ANOVA provides researchers with a separate F tests for the interaction effect, between-groups main effect, and within-groups main effect each evaluated with specific degrees of freedom. Within this example, the primary effect of interest is the interaction effect, evaluating whether BDI-II scores changed differently within the new TCA group compared to the amitriptyline group from pretest to posttest. Similar to factorial ANOVA discussed above, only if the interaction effect is nonsignificant can the researcher evaluate the statistical significance of overall group mean difference or between-group main effect and the overall change in BDI-II scores or within-group main effect. That is, a statistically significant interaction effect indicates that the change in BDI-II scores from pretest to posttest changed differently in the group receiving the new TCA group compared to the group receiving amitriptyline or vice versa. Stated another way, the reduction in symptoms from pretest to posttest was dependent on whether the patient received the new TCA or amitriptyline. In this example, the researcher’s hypothesis would be supported by a statistically significant interaction effect; that is, the new TCA was more effective at reducing the symptoms associated with clinical depression compared to amitriptyline. Although the example above was for a 2 (group: new TCA versus amitriptyline) × 2 (measurement: pretest versus posttest) design, a mixed between-within ANOVA can be used for a design with any number of IVs with any number of levels or repeated measures. This is often seen in the literature. For example, consider the pretest-posttest study evaluating the effectiveness of three treatment groups (e.g., new TCA, amitriptyline, and placebo) in reducing symptoms of clinical depression. This would be considered a 3 × 2 design. Or, consider the same study evaluating for additional gender differences within these three treatments. This would be considered a 3 × 2 × 2 design. It must be noted that as the number and levels of the IVs increase so does the complexity of interpreting results. Thus, extreme care must be taken when interpreting results and implementing suggestions supported by these types of designs. Consultation with an individual well versed in research methodology and statistical analysis is advised prior to implementing findings into an evidence-based practice. Because a mixed between-within ANOVA is an extension of factorial between-groups ANOVA and repeated measures ANOVA, post hoc tests or pairwise comparisons may be  required for any IV with three or more levels. That is, when there are more than three levels of the between-groups IV (e.g., new TCA, amitriptyline, and placebo), the

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between-groups main effect is an omnibus test. A statistically significant between-groups main effect indicates that a statistically significant difference in BDI-II scores exists, but does not indicate specifically which groups differ significantly. Further, with three or more repeated measures, a statistically significant within-groups main effect indicates a difference between repeated measures, but fails to indicate specifically which measurements differ significantly. The post hoc tests and pairwise comparisons for factorial between-groups ANOVA and repeated measures ANOVA, respectively, are also appropriate for a mixed between-within ANOVA. The results of a 2 × 2 mixed between-within ANOVA with no assumption violations for a nonsignificant interaction effect and statistically significant between- and withingroups main effects is presented as follows: The results of a 2 (group: new TCA versus amitriptyline) × 2 (measurements: pretest versus posttest) mixed between-within ANOVA failed to indicate a statistically significant interaction (F1,18 = 1.215, p > 0.05). However, both main effects were statistically significant. Overall, patients receiving the new TCA had lower BDI-II scores compared to patients receiving amitriptyline (F1,18 = 27.97, p < 0.05; mean = 29.41 versus 47.50, respectively). Further, an overall decrease in depressive symptoms was indicated from pretest to posttest (F1,18 = 24.74, p < 0.05; mean = 49.86 versus 35.72, respectively). With a statistically significant interaction effect, results of the mixed between-within ANOVA with no assumption violations are presented as follows: The results of a 2 (group: new TCA versus amitriptyline) × 2 (measurements: pretest versus posttest) mixed between-within ANOVA indicated a statistically significant interaction effect (F1,18 = 10.37, p < 0.05). Simple main effects were assessed to identify statistically significant treatment differences at pretest and posttest individually. Results indicated no statistically significant difference between the new TCA and amitriptyline at pretest (mean = 48.53 versus 50.23, respectively). However, at posttest, a statistically significant difference was indicated, with the new TCA having significantly lower BDI-II scores compared to amitriptyline (mean = 24.63 versus 47.53, respectively). The assumptions of a mixed between-within ANOVA include: 1. The DV is measured on an interval or ratio scale. 2. All DV measurements are associated. 3. The sampling distribution of means at each level of the IV(s), collapsed across the repeated DV measurements, is normal. a. This can be ensured by applying the central limit theorem. 4. Homogeneity of variance for all DV measurements within each level of the IV(s) is ensured. a. That is, the variance within each group is similar. A crude indicator of a violation of this assumption (i.e., heterogeneity) is the ratio of the largest variance to

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smallest variance being greater than 10:1.22 For example, most studies do not provide the variance for each variable; however, the standard deviation is reported consistently. Remember, variance is simply the standard deviation squared. Thus, consider two variables with standard deviations of 5 and 10. The homogeneity of variance assumption can be tested by squaring the standard deviations (i.e., 52 = 25 and 102 = 100, respectively) and finding their ratio (i.e., 100/25 = 4). In this case, the ratio is less than 10:1; thus, the assumption is not violated. 5. Sphericity is ensured for designs with three or more repeated measurements. a. This is a complex assumption discussed briefly above for one-way repeated measures ANOVA. In general, sphericity is violated when the variance of the differences between measurements are not similar.

Nonparametric Tests Wilcoxon signed-rank test The Wilcoxon signed-rank test (also known as signed-rank test) is the nonparametric alternative to a paired-samples t-test. The test is used typically to assess for differences between two repeated measurements or two matched groups when distributional assumptions are violated or when the DV is measured on an ordinal scale. The signed-rank test is based on ranked difference scores (e.g., difference between pretest and posttest), with the highest difference score receiving the highest rank and the lowest difference score receiving the lowest rank. For example, consider single group pretest-posttest study evaluating the secondary effect of weight loss in pounds while on exenatide therapy in a sample of 20 Type 1 DM patients. Prior to initiating exenatide therapy, all patients are weighed (i.e., pretest). At the end of a 1-year study period, patients are weighed again (i.e., posttest). For this study, the DV has numerous outliers; thus, the signed-rank test is used in lieu of paired-samples t-test to assess for a change in patient weight from pretest to posttest. The result of a statistically significant signed-rank test is presented as follows: The results of the signed-rank tests indicated a statistically significant decrease in body weight from pretest to posttest (z = 2.32, p < 0.05). The assumptions of the signed-rank test include: 1. The DV measured repeatedly on an ordinal, interval, or ratio scale. 2. The two DV measurements are associated. 3. The two measurements come from populations with the same median. 4. There should not be a large number difference scores equal to zero. a. That is, the number of participants having no change (i.e., difference score of zero) should be low.

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Friedman two-way ANOVA by ranks The Friedman two-way ANOVA by ranks test (also known as Friedman’s test) is the nonparametric alternative to the one-way repeated measures ANOVA and is an extension of the signed-rank test to a situation with three or more repeated measurements. Similar to the other nonparametric tests, it is most often used when distributional assumptions are violated or the DV is measured on an ordinal scale. The Friedman test is based on ranked data, with higher scores receiving higher ranks, and is used to assess for statistically significant differences between repeated measurements. For example, reconsider the exenatide example described in the Wilcoxon SignedRanks section above. Briefly, the example consisted of a one-time pretest-posttest study evaluating the secondary effect of weight loss in pounds while on exenatide therapy in a sample of 20 Type 1 DM patients. To extend this design to be applicable to Friedman’s test, consider a study where patients are weighed prior to initiating exenatide therapy, 6 months after initiation, and 1-year after initiation. Thus, each patient is weighed on three occasions. Because the distribution of weight has numerous outliers, Friedman’s test is employed. The results of a statistically significant Friedman’s test are presented as follows: The results of Friedman’s test indicated statistically significant differences in body weight between the three repeated measurements (χ22 = 15.21, p < 0.05). Similar to the one-way repeated measures ANOVA, Friedman’s test is an omnibus test. That is, it assesses whether a statistically significant difference exists between the repeated measurements but does not indicate specifically which measurements differ significantly. Thus, a series of post hoc tests are required. For the example above, three signed ranks tests are required to test for differences between measurements: pretest versus 6 months, pretest versus 1 year, and 6 months versus 1 year. The results of a statistically significant Friedman’s test including the additional post hoc tests are presented as follows: The results of Friedman’s test indicated statistically significant differences between the three repeated measurements (χ22 = 15.21, p < 0.05). Post hoc signed-rank tests indicated a statistically significant decrease in body weight from pretest to 6-month follow-up (z = 2.65, p < 0.05), with no statistically significant difference between the 6-month and 1-year follow-up (z = 0.51, p > 0.05). Thus, results suggest weight loss occurred rapidly, within 6 months of initiating exenatide therapy, and was sustained through 1 year of therapy. The assumptions of the Friedman test include: 1. The DV is measured repeatedly on an ordinal, interval, or ratio scale. 2. All DV measurements are associated. 3. The DV measurements come from populations with the same median.

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The sign test The sign test is another nonparametric alternative to the paired-samples t-test. Similar to the signed-rank test, the sign test is used typically when distributional assumptions are violated or when the DV is measured on an ordinal scale. The sign test is used to assess for differences between two repeated measurements or two matched groups. It must be noted that the sign test is typically less powerful than the signed-rank test as the signedrank test uses more information from the data to calculate the test statistic. Nevertheless, the sign test is presented here because it is seen in the literature; however, in most situations, the signed-rank test should have been used. For example, consider a pretest-posttest study to evaluate change in BMI following a physical activity intervention in a sample of 20 third grade students. In this study, BMI was measured at the beginning of the school year (i.e., pretest) and again after school year was complete (i.e., posttest). The sign test assesses whether statistically significant change occurred between the two measurements. The results of a statistically significant sign test are presented below. Notice that no test statistic is presented, only a p value. The results of the sign test indicated a statistically significant decrease in BMI from pretest to posttest (p < 0.05). The assumptions of the sign test include: 1. The DV is measured repeatedly on an ordinal, interval, or ratio scale. 2. The two DV measurements are associated.

The McNemar test of change The McNemar test of change (also known as McNemar’s test) is an extension of the chisquare and Fisher’s exact test when participants are measured on two separate occasions and assesses the statistical significance of observed changes between the two repeated measurements. McNemar’s test is only applicable to a DV measured on a nominal scale; however, continuous variables can be artificially dichotomized, with the understanding that information will be lost.27,41 McNemar’s test is often used for pretest-posttest studies to assess change following an intervention or treatment. For example, consider a study to determine the effectiveness of an influenza vaccination across two consecutive flu seasons. At the beginning of the first flu season, 20 participants are randomized to receive either vaccination or placebo with ten in each group. At the end of the first flu season, participants are asked whether they were diagnosed with the flu or not (i.e., yes versus no). Then, at the beginning of the second flu season, participants who received the vaccination originally will receive placebo and those who received placebo originally will receive the vaccination. At the end of the second flu season, participants are asked again whether they were diagnosed with flu. McNemar’s test is used in this study to statistically test whether the flu

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vaccination was effective, where participants diagnosed with flu while taking placebo should not have developed flu with the vaccination. That is, the participant’s outcome changed depending on the treatment received. A critically important caveat is that McNemar’s test only considers participants who changed between the two repeated measurements, with participants who did not change removed from analysis. Thus, if a researcher believes that change will be rare, the McNemar test may be inappropriate because the statistical power of this test may be extremely reduced due to the sample size decrease from removing participants who did not change. Based on the example above, the results of a statistically significant McNemar test are presented below. Notice that no test statistic is provided, only the p value. The results of a statistically significant McNemar’s test indicated a statistically significant change between treatment and placebo (p < 0.05), with the vaccination significantly reducing influenza diagnoses compared to placebo. The assumptions of the McNemar test include: 1. The DV is measured repeatedly on a dichotomous scale. 2. The two DV measurements are associated.

The Cochran Q test The Cochran Q test (also known as Cochran’s Q) is an extension of McNemar’s test to situations where participants are measured repeatedly on three or more separate occasions.27 Similar to McNemar’s test, the DV must be measured repeatedly on a nominal scale. In the biomedical literature, Cochran’s Q is often used to assess stability of a treatment over time or to compare the effectiveness of several treatments. For example, consider a study evaluating the effectiveness of the combination treatment sildenafil and psychotherapy in reducing symptoms of erectile dysfunction (ED).42 Eight patients with psychogenic ED attended weekly psychotherapy sessions and ingested 50 mg of sildenafil citrate orally as needed over a 6-month period. Symptoms of ED were assessed at baseline, 6 months (i.e., end of treatment), and at a 3-month posttreatment follow-up. For this study, Cochran’s Q was used to evaluate a change in the stage of remission for patients dichotomized into ED versus no ED. A statistically significant finding indicated change from baseline and the possibility of sustaining effects at posttreatment follow-up. That is, all patients were diagnosed with ED at baseline, thus a statistically significant change indicates patients indicated remission of ED symptoms at some point. In the literature, the results of the Cochran Q test may be presented with a χ2 statistic and a p value; however, in other studies, the results may only present a p value. Although failing to include the test statistic provides less information, it does not necessarily damage the integrity of results. The result of a statistically significant Cochran’s Q for the example above is presented as follows:

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The results of Cochran’s Q indicated a statistically significant change in psychogenic ED symptoms from baseline (p < 0.05) suggesting a combination of psychotherapy and 50 mg sildenafil are effective in reducing ED symptoms. Because Cochran’s Q is used to assess change over three or more repeated measures, it is an omnibus test. That is, the test will determine whether a statistically significant change occurred between the repeated measurements, but will not indicate where the change occurred. Thus, post hoc tests are required. For Cochran’s Q, a series of McNemar tests comparing each repeated measurement serve as post hoc tests. In this example, three separate McNemar tests are required comparing baseline versus 6 months, baseline versus posttreatment follow-up, and 6 months versus posttreatment follow-up. The results of a statistically significant Cochran’s Q including results of post hoc McNemar tests are presented as follows: The results of the Cochran Q test indicated a statistically significant change in psychogenic ED symptoms from baseline (p < 0.05). Post hoc McNemar tests indicated statistically significant changes from baseline at 6 months as well as at posttreatment follow-up (all p < 0.05). These results suggest the combination of psychotherapy and sildenafil are effective in reducing ED symptoms and this effect continued to reduce ED symptoms up to 3 months following treatment completion. The assumptions of the Cochran test include: 1. The DV measured repeatedly on a dichotomous scale. 2. All DV measurements are associated.

TESTING FOR RELATIONSHIPS OR ASSOCIATIONS When exploring the association or relationship between two or more variables, two specific types of analyses are employed—correlation or regression. These analyses are applied to determine the magnitude and direction of an association or relationship. Correlation analysis indicates the co-relationship of two variables. That is, correlation described how one variable changes in relation to another. It is critically important to note that correlation cannot imply causation. For example, consider the positive association between creatinine and BUN. In most cases, as creatinine increases so does BUN, but increasing creatinine does not cause BUN to increase or vice versa. Instead, the cause of the BUN and creatinine increase might be due to renal failure. Regression analysis is a type of correlational analysis used to predict the value of one variable from the value of another variable. In this type of analysis, researchers attempt to determine the amount of variance in the DV that is explained by the IVs or covariates. Note that regression analysis can also permit multiple IVs and/or covariates measured on any scale. With the inclusion of multiple IVs or covariates this type of analysis is often

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referred to as multivariable regression analysis. While the definition of an IV and covariate is not always concrete, it is easier to think of an IV as the primary variable of interest and a covariate as a variable correlated with the DV, but not of specific interest. For example, consider a multivariable analysis assessing the effect statin use (IV) had on all cause mortality due to heart failure (DV) during a 5-year study period after statistically controlling for age, gender, race, comorbid conditions, and concurrent medications (covariates). The covariates may explain the reason a patient died, but are not of specific research interest. This section begins by discussing bivariate, or two variable, techniques followed by multivariable techniques. The discussion in this section will progress in a similar fashion to the statistical tests already presented where parametric tests will be discussed first, followed by the nonparametric alternatives.

Parametric Tests Pearson’s product-moment correlation Pearson’s product-moment correlation (also known as Pearson’s correlation or Pearson’s r) is one of the most commonly used correlation measures. It measures the direction and strength of a linear relationship between two continuous variables. Pearson’s r ranges from −1 and + 1, with r of 0 indicating no relationship. That is, as the correlation is stronger as it approaches −1 or 1. A positive correlation (e.g., 0.30 or 0.99) indicates that as the values of one variable increase so do the values of the other variable, while a negative correlation (e.g., −0.50 or −0.80) indicates that as the values of one variable increase, the values of the other variable decrease. Pearson’s r tests whether the correlation of two variables is different from 0 (i.e., no relationship); thus, a statistically significant r indicates the slope of the linear relationship is not horizontal. For example, research has shown a moderate, but statistically significant, positive correlation (r = 0.25) between weight in kilograms and platelet count in men aged 20 to 55.43 Thus, in men, as weight increases, so does platelet count. However, the magnitude of this increase varies from person to person. That is, for any individual man, a 1-kg increase in body weight may indicate an increase in platelet count that is different from the platelet count increase for another man. It should be noted the value of r is dimensionless because it is based on standardized scores. That is, measuring weight in pounds or kilograms in the example above will not change the value of the correlation. Finally, be aware that r is substantially affected by outliers, so researchers must identify and remove them from analysis or use the nonparametric Spearman’s rho discussed below. The relationship between two variables can be assessed visually by plotting the data on a scatterplot. In fact, this practice is highly recommended.5 The magnitude of the correlation is directly related to the strength of the linear relationship. Take a moment to consider Figures 8–13 and 8–14. In Figure 8–13, the value of Pearson’s r is approximately 1 indicating

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a near perfect relationship between the two variables. Notice the dots on the scatterplot lay near the best fit line and that the slope of this line is fairly steep. The positive correlation coefficient indicates that as the values of continuous variable 1 increase so do the values of continuous variable 2. In Figure 8–14, Pearson’s r is approximately 0. Here, notice the dots are scattered all over the plot with no real direction, and the best fit line is almost perfectly horizontal, which indicates no relationship between the two continuous variables. 60

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Figure 8–14. Scatterplot showing no correlation.

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To highlight the substantial effect outliers have on Pearson’s r, take a moment to draw an outlier on Figure 8–13, say, a value of 45 for continuous variable 1 and a value of 25 for continuous variable 2. Visualize what effect this outlier has on the previously strong positive correlation and how it would pull the best fit line toward horizontal significantly weakening the correlation. The result of a statistically significant Pearson’s product-moment correlation with no assumption violations using the example above is presented as follows: The results of Pearson’s product-moment correlation indicates a statistically significant moderate relationship between body weight in kilograms and platelet count (r81 = 0.252, p < 0.05) indicating body weight increased in concordance with platelet count. Assumptions of Pearson’s product-moment correlation include: 1. Both variables are measured on an interval or ratio scale. 2. There are no outliers. 3. The relationship between the variables is linear. 4. Homoscedasticity is ensured. a. The assumption states that the variability around the best fit line of the linear relationship is the same for all data. A violation of this assumption can be seen within a scatterplot. For example, consider a scatterplot where the lower values for a variable fall near the best fit line and higher values for this same variable fall far from the best fit line. In this situation, the variability around the line is not constant. The tenability of this assumption is difficult to ascertain in a journal article; thus, ensure the authors noted that it was tested. 5. The observations are independent. a. That is, each participant provides one, and only one, observation (i.e., data or response) for each variable.

Simple linear regression As stated in the previous section, the magnitude or size of the correlation is directly related to the strength of the linear relationship. The pattern of this linear relationship is typically indicated by the regression line, which is another name for the best fit line presented in Figures 8–13 and 8–14. The regression line is a best fit line describing how the continuous DV changes as values of the IV change. Similar to Pearson’s r, the statistical test in simple linear regression is whether the slope of the regression line is statistically different from zero, or, stated another way, whether the regression line has no slope or is horizontal. Simple linear regression, however, takes Pearson’s r one step further, where the regression line is used to predict values of the DV for a given value of the IV.21 This is incredibly useful to evidence-based practitioners looking to implement findings into their practice.

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The algebraic linear regression equation is: ˆy = a + bx. Here, a is the intercept of the regression line with the y-axis, b is the slope of the regression line, x is the value of the IV, and ˆy (pronounced y-hat) is the predicted value of the DV. The intercept is interpreted as the predicted value of the DV when the value of the IV is zero. The slope is interpreted as the overall change in the DV for a one-unit increase in the IV. Further, it is important to note that the value of Pearson’s correlation between the DV and IV is incorporated into the mathematical equation for the slope. As an example, consider the association between systolic blood pressure measured in millimeters of mercury (mm Hg) and height in centimeters for 100 children aged 5 to 7 years.44 The results of this study indicated a positive Pearson correlation between height and systolic blood pressure of 0.33. Moving beyond basic correlation, the researchers used a simple linear regression analysis to predict a child’s systolic blood pressure from their height. Results indicated an intercept value of 46.28 mm Hg and a slope of 0.48. Using these values, the linear regression equation would be: ˆy = 46.28 + (0.48*Height). Using the interpretation of the intercept and slope provided above, the intercept value of 46.28 is the predicted systolic blood pressure for a child 0 cm tall, whereas the slope indicates that a 1-cm increase in height increases systolic blood pressure 0.48 mm Hg. From this interpretation, it is obvious that a child with a height of 0 cm is impossible. This is a prime example of the awareness readers must have when interpreting the intercept. That is, unless it makes theoretical sense to have a meaningful zero point for the IV, the interpretation of the intercept is never useful. This situation, however, should not suggest the intercept is meaningless to prediction. Instead, the intercept is simply a starting point for predicting the outcome. For example, say we want to predict systolic blood pressure for a child that is 115 cm tall; the linear regression equation becomes: ˆy = 46.28 + (0.48*115), which equals 101.48 mm Hg. It is important to note that the predicted values of the DV are rarely identical to the actual observed values. That is, a child 115 cm tall in this sample may actually have a systolic blood pressure of 105 mm Hg, but have a predicted value of 101.48 mm Hg. The difference between the actual and predicted scores is referred to as a residual value or residual error. For the example child above, the residual value is 3.52 mm Hg (i.e., 105– 101.48). Residual values are always calculated for all participants included in the regression analysis, and one of the assumptions of linear regression is that the residual values follow a normal distribution; this is where the assumption of normality originates for all parametric statistical tests. The tenability of this assumption is a key indicator of the reliability of the results. Therefore, if a researcher fails to describe the distribution of residuals alongside their results, the study should be read and interpreted with caution. In addition, the interpretation of slope used in the above example is only appropriate for IVs measured on a continuous scale. If instead the IV is categorical, interpretation is slightly different. For example, consider replacing height in the example above with the

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dichotomous IV gender. In this situation, the researcher must specify which level of gender will serve as the reference or comparison category that is coded 0 for analysis. That is, specify which level of the IV the calculated slope represents. Note that every published study should indicate which group served as the reference category, and if the authors fail to provide a reference category, interpretation becomes impossible. Continuing, if females are specified as the reference category, the slope for gender provides the overall difference in predicted systolic blood pressure for males compared to females. Interpretation follows this logic. For example, using an example similar to the above, say a simple linear regression analysis indicated the intercept was 115.54 and the slope for gender was 15.20 with females considered the reference category. The new linear regression equation would be: ˆy = 115.54 + 15.20*Male. Thus, the intercept value of 115.54 now represents the average systolic blood pressure for a women (i.e., when Male = 0) and the slope indicates that the predicted value of systolic blood pressure will be 15.20 mm Hg higher for a man (i.e., when Male = 1) compared to a woman. Admittedly, these interpretations can be confusing, but understanding this concept is critically important to proper interpretation of study results. The primary test used in simple linear regression is an omnibus between-groups ANOVA. It may seem esoteric, but linear regression and ANOVA are mathematically equivalent. The omnibus ANOVA provides an F test indicating whether the IV explains a statistically significant amount of variance in the DV. Stated another way, the omnibus test determines whether the IV reliably predicts the DV. Only if the ANOVA is statistically significant is the slope of the individual IV interpreted. Most research studies will provide the results of the ANOVA prior to presenting the slope of the IV. Further, the ANOVA results presented in simple linear regression will be presented identically to the one-way between-groups ANOVA examples discussed above. The statistical significance of the IV will most often be presented with the regression slope and potentially an associated t value. The slope is critical to proper interpretation of any regression analysis; thus, if the slope is not presented in the narrative portion or in a table complete interpretation of the regression analysis is impossible and the study is essentially useless. Finally, the amount of variance in the DV explained by the IV must also be considered. That is, how much of the reason why a participant has a particular value on the DV is attributable to their IV value. As a side note, the word “explained” should not and does not imply causality, as causality in correlational studies is extremely difficult to determine. The amount of variance explained in simple linear regression is quantified by an effect size estimate termed the coefficient of determination. This coefficient is calculated by squaring Pearson’s r between the IV and DV (i.e., r2). In the literature it will often be referred to simply as r2, or identically as R2. Note that how this value is referred to in a study is dependent on the researcher, but regardless of whether r2 or R2 is reported, their values will be identical and, thus, interpreted identically. The coefficient will often be

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presented as a proportion, ranging from 0 to 1, with higher values indicating more reliable prediction. From the example above, remember the correlation between a child’s height and systolic blood pressure was 0.33. Thus, approximately 0.11 (i.e., 0.332) of the child’s measured systolic blood pressure can be explained by the child’s height. Said another way, approximately 11% of the reason a child has a particular systolic blood pressure value is explained by his or her height. In the literature, the result of a statistically significant simple linear regression analysis with no assumption violations will be presented as follows: The results of a simple linear regression analysis indicated a child’s height significantly predicts systolic blood pressure (F1,98 = 12.03, p < 0.05, r2 = 0.11), with a 1-cm increase in height resulting in a 0.48 mm Hg increase in systolic blood pressure. The assumptions of simple linear regression include: 1. The DV is measured on an interval or ratio scale. 2. There are no outliers. 3. The relationship between the DV and IV is linear. 4. Homoscedasticity is ensured. a. The assumption states that the variability around the regression line is the same for all data. A violation of this assumption can be seen within a scatterplot. For example, consider a scatterplot where the lower values for a variable fall near the regression line and higher values for this same variable fall far from the regression line. In this situation, the variability around the regression line is not constant. The tenability of this assumption is difficult to ascertain in a journal article; thus, ensure the authors noted that it was tested. 5. Residuals are distributed normally. a. Residuals are the difference between the actual and predicted values. Authors will need to state that they tested this assumption. 6. The observations are independent. a. That is, each participant provides one, and only one, observation (i.e., data or response) for each variable.

Multivariable linear regression Multivariable linear regression (also known as multiple linear regression) is an extension of simple linear regression for designs with one continuous DV and multiple IVs or covariates measured on any scale. Remember, an IV is defined as an explanatory variable of specific research interest, while a covariate is a nuisance variable that is significantly associated with the DV but not of specific research interest. That is, covariates are typically included because they are related to the DV or because previous research has indicated they are important. In multiple linear regression, IVs can be any combination of continuous or discrete variables (e.g., height, gender). Further, this analysis is often a better option than simple linear

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regression because the inclusion of additional IVs often explains a higher percentage of variance in the DV. That is, multiple linear regression produces higher R2 values. For example, previous research has shown a statistically significant negative Pearson’s correlation between serum 25-hydroxyvitamin D (25OHD; ng/mL) and serum parathyroid hormone (PTH; pg/mL) of −0.28.45 Based on this correlation, the percentage of variance in serum PTH explained by serum 25OHD is 8% (i.e., −0.282). That is, 8% of the reason a participant has a predicted serum PTH value is due to their serum 25OHD level. In an effort to increase this percentage of variance explained, a new study is designed to determine the effect serum 25OHD has on serum PTH after statistically adjusting for age, BMI, total calcium intake, and serum creatinine. Thus, a multiple linear regression analysis will be used to determine whether there is an effect of serum 25OHD on serum PTH over and above the effect of the covariates. That is, multiple linear regression assesses the unique correlation between 25OHD and serum PTH after removing the effects already accounted for by age, BMI, total calcium intake, and serum creatinine. The percentage of variance explained in multiple linear regression is always referred to as R2, where the R indicates the multiple correlation. That is, R is the multivariate extension of Pearson’s r and is defined as the combined or total correlation between all IVs and the DV. Similar to Pearson’s r, R ranges from −1 to 1, with 0 indicating no relationship. Thus, as R approaches −1 or 1, the association between the IVs and DV becomes stronger. In addition, most studies will also present an adjusted R2 value, sometimes labeled R2adj in the literature. Adjusted R2 is interpreted exactly the same as R2, but it is adjusted for the sample size used in the study. When reading a study, comparing R2 and adjusted R2 is incredibly useful to interpretation, as large differences between the two indicates significant issues with the analysis, such as inadequate sample size, which essentially render the regression model useless and not generalizable to the population. Finally, it should be noted a multiple linear regression model will never explain 100% of the variance in the DV. However, do not disregard studies reporting low values of R2 because the definition of what constitutes a large R2 value varies by research arena. That is, lower R2 values are expected when using human participants because measurement error is usually high. For example, consider a study using participant self-reported daily calorie intake. Large R2 values are expected for bench research studies because in well conducted bench research measurement error is typically not an issue. For example, think about a biomedical research study using analytic chemistry. In general, the results of a multiple linear regression model are interpreted in an almost identical fashion to simple linear regression. As a result, please reconsider the section on simple linear regression if necessary. Similar to simple linear regression, multiple regression produces a regression equation allowing for prediction of DV values based on the y-intercept and the slope of the regression line for each IV or covariate. Briefly, the intercept is the predicted value of the DV when all values of the IVs and covariates are 0,

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while the slope quantifies the change in the predicted DV with a one-unit increase in the IV or covariates. Again, the regression equation is incredibly useful to evidence-based practitioners looking to implement findings into their practice. For example, consider the multiple regression example above, where serum PTH was predicted from 25OHD and a set of covariates. Based on the regression equation from this study, the practitioner can provide the patient with empirical evidence regarding which variables (i.e., age, BMI, total calcium intake, serum creatinine, and serum 25OHD) to increase or decrease, if possible, in an effort to optimize serum PTH levels and increase bone production. The overall test of the multiple linear regression model is an omnibus betweengroups ANOVA, which indicates at least one of the IVs or covariates significantly predicts the DV. However, this omnibus test fails to indicate which IVs or covariates significantly predict the DV. Thus, the statistical test for each IV or covariate is considered. Each of the statistical tests for the IVs and covariates can be considered similar to a post hoc test; however, unlike the post hoc tests discussed for ANOVA-type models above, alpha remains unadjusted. In most studies, the results of the individual IVs or covariates are presented as regression slope or t values. Note that regardless of which result an author presents, the p values will be identical. Further, interpretation of the slopes for the individual coefficients is also slightly different compared to simple linear regression. In multiple linear regression, interpretation of a particular IV or covariate is statistically adjusted for all other IVs and covariates in the regression model similar to ANCOVA. An example may help clarify this information. Consider a situation where the result of a statistically significant multiple linear regression analysis based on the example above indicates 25OHD has a statistically significant slope of −1.5 pg/mL. Because the analysis is multivariable, this slope must be interpreted considering all covariates included in the model. Thus, the slope of −1.5 pg/mL indicates that after adjusting for age, BMI, total calcium intake, and serum creatinine, a 1-ng/mL increase in 25OHD decreases predicted serum PTH 1.5 pg/mL. Based on the example, the results of a statistically significant multiple linear regression analysis with no assumption violations are presented below. Notice the effects of statistically significant covariates (i.e., BMI and serum creatinine) are also described; however, authors will vary on which covariates, if any, they choose to interpret. The results of a multivariable linear regression analysis indicated age, BMI, total calcium intake, serum creatinine, and 25OHD significantly predicted serum PTH (F5,472 = 21.82, p < 0.05, adjusted R2 = 0.18). After adjusting for covariates, 25OHD significantly predicted serum PTH (slope = −1.5, p < 0.05). That is, with all else held constant, a 1 -ng/mL increase in serum 25OHD resulted in a 1.5-pg/mL decrease in serum PTH. Regarding the individual covariates, after adjustment, increases in BMI and serum creatinine (slope = 0.75 and 2.12, respectively, both p < 0.05) resulted higher serum PTH levels. Finally, after adjustment, age and total calcium intake were not associated with serum PTH.

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The assumptions of multiple linear regression include: 1. The DV is measured on an interval or ratio scale. 2. Absence of multicollinearity is ensured. a. That is, no Pearson’s r between any IVs and covariates should be greater than 0.90, as correlations this high indicate the variables are redundant. That is, high correlations indicate the variables may be measuring the same construct. Including redundant variables will significantly bias results. Most published studies provide Pearson correlations between the DV, IVs, and covariates; thus, a violation of this assumption is easy to identify. 3. There are no outliers. 4. The relationship between the DV and IVs and between the DV and covariates is linear. 5. Homoscedasticity is ensured. a. The assumption states that the variability around the regression line is the same for all data. A violation of this assumption can be seen within a scatterplot. For example, consider a scatterplot where the lower values for a variable fall near the regression line and higher values for this same variable fall far from the regression line. In this situation, the variability around the regression line is not constant. The tenability of this assumption is difficult to ascertain in a journal article; thus, ensure the authors noted that it was tested. 6. Residuals are distributed normally. a. Residuals are the difference between the actual and predicted values. 7. The observations are independent. a. That is, each participant provides one, and only one, observation (i.e., data or response) for each variable.

Nonparametric Tests Spearman rank-order correlation coefficient The Spearman rank-order correlation coefficient (rs), also known as Spearman’s rho (ρ), is the nonparametric alternative to Pearson’s r. This correlation is used when two continuous variables have outliers, when the variables are measured on an ordinal scale, or when the relationship is nonlinear. Similar to the other nonparametric statistical tests discussed previously, this correlation is based on rank ordered data as opposed to the actual values. The value of rs ranges between −1 and 1, with 0 indicating no association. Thus, as rs approaches −1 or 1, the association between the two variables becomes stronger. A statistically significant rs indicates that the association is significantly different from 0. As an example, consider a study assessing the association between triglyceride content and lag time in LDL oxidation in a sample of 18 renal transplant patients.46

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Spearman’s rank-order correlation was used in this study because outliers were identified in the sample, and because the sample was small, removing outliers was not a viable option. The study found a statistically significant negative rs of −0.502, suggesting that as triglyceride content increased, LDL oxidation decreased. Based on the example above, the result of a statistically significant Spearman rankorder correlation coefficient is presented as follows: The Spearman rank-order correlation analysis was employed in lieu of Pearson’s correlation due to the presence of outliers and small sample size. Results indicated a statistically significant negative association between triglyceride content and lag time in LDL oxidation (rs = −0.502, p < 0.05), which suggest increases in triglyceride content translates into decreases in LDL oxidation. The assumptions of the Spearman rank-order correlation coefficient include: 1. The two variables are measured on an ordinal, interval, or ratio scale. 2. The relationship between the two rank-ordered variables is linear. a. Although the relationship between the two variables based on their actual values may be nonlinear, the relationship based on rank-ordered data must be linear. This assumption typically cannot be tested by what the authors provide in the narrative. Thus, when authors fail to indicate whether the assumption was tested results should be viewed with caution. 3. The observations are independent. a. That is, each participant provides one, and only one, observation (i.e., data or response) for each variable.

Logistic regression The interpretation of a logistic regression analysis is similar to linear regression; thus, a basic understanding of the interpretation of simple and multiple linear regression is extremely useful. Please reconsider reading the sections discussing simple and multivariable linear regression as much of the material discussed here is simply an extension of the material described in detail previously. Logistic regression is used when the DV is measured on a dichotomous scale and the relationship between the DV and IV is nonlinear. This analysis is ubiquitous in the biomedical sciences. It is important to note that in any logistic regression analysis, the measurement scale of the DV is always considered unordered. That is, the dichotomous DV is always considered to be measured on a nominal scale. While the logistic regression analyses discussed here are only for a dichotomous DV, an extension of logistic regression is available for a categorical DV with three or more categories. This analysis is termed multinomial logistic regression, with the definition of multinomial being multiple nominal categories. Interpretation of results from this analysis is similar to the analyses

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discussed in this section and interested readers are encouraged to consider the suggested readings at the end of the chapter. As an example of a design requiring a simple logistic regression analysis, consider a 5-year study designed to assess the effect that the duration of statin use measured as percentage of time on any statin during the study period has on all-cause mortality in a sample of Veterans Administration patients previously experiencing congestive heart failure. Note that the DV is dichotomous (i.e., dead versus alive). Further, a simple logistic regression analysis can always be extended to a multivariable analysis by including additional IVs and covariates in an effort to explain more of the reason why patients experienced the outcome of interest. For example, consider a multivariable extension to the study above where the effect duration of statin use has on all-cause mortality is assessed after controlling for age, race, gender, concurrent medications, and comorbid conditions. For all logistic regression models, researchers must choose a reference category for the DV. When identified, the reference category is used as a comparison group for the primary outcome of interest. In most situations, the reference category is typically the category determined by the researcher to be of less specific interest. For example, consider all-cause mortality, a dichotomous DV (i.e., dead versus alive). Most studies are interested in the individuals who died; essentially the researcher wants to identify the primary reasons for death. Thus, with patients alive at the end of the study period considered the reference category, all regression slopes are calculated for patients who died compared to patients who lived. Thus, the first step in properly interpreting the results of logistic regression analysis is to identify the primary outcome of interest and the reference category within the DV. It should be noted that most authors will not explicitly identify the primary outcome of interest or the reference category; however, this information can be obtained easily as all results and interpretations are typically written in relation to the primary outcome of interest. Similar to linear regression, a logistic regression analysis provides a regression equation that can be used to predict the probability of experiencing primary outcome of interest. Briefly, the regression equation contains a y-intercept and slope values for all IVs included in the analysis. This equation is interpreted slightly different from linear regression because the association between the DV and IV is nonlinear. That is, because probability is bounded between 0 and 1, the slope has to essentially shut off at these bounds. However, the usefulness of the equation is the same. That is, the equation can be used to assist evidence-based practitioners in instructing patients regarding what changes need to be made to optimize or prevent a specific outcome. The primary statistical test in logistic regression determines whether the logistic model including all IVs or covariates better predicts the probability of experiencing the outcome of interest compared to the model with no IVs or covariates. That is, a logistic regression analysis determines whether the IVs significantly predict the primary outcome

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of interest. Similar to linear regression, this overall test is an omnibus chi-square test. If this omnibus chi-square test is statistically significant, the statistical significance of each IV or covariate is assessed and interpreted. These tests of individual predictors can be thought of as post hoc tests, and typically no adjustment is made to alpha. When interpreting the results for individual predictors, authors typically provide two values, the slope and odds ratio. The slope is interpreted similar to linear regression; however, slopes in logistic regression indicate changes in the log-odds (also known as logits) of experiencing the outcome of interest. That is, a one-unit increase in the IV indicates a change in the predicted log-odds of experiencing the primary outcome of interest. While a full description of log-odds or logits is beyond the scope of this chapter, they can be thought of simple as a linear transformation of probability. That is, after transformation, log-odds or logits are linear; thus, their values can be interpreted similarly to slopes in linear regression. For example, reconsider the 5-year study assessing the effect duration of statin use, measured as percentage of time on any statin during the study period, has on all-cause mortality. Say, the slope for statin use is −0.25. With death considered the primary outcome of interest and alive serving as the reference category, this slope suggests that a 1% increase in statin use during the study period resulted in a 0.25  unit decrease in the log-odds of dying. Note this was a decrease because the slope was negative. Based on this interpretation, a common question is, what does a 0.25 unit decrease in log-odds mean? While the slope is integral in producing the regression equation, the interpretation in log-odds can be fairly convoluted. Thus, logistic regression provides an alternative value that some individuals find easier to interpret—the odds ratio. The odds ratio produced by a logistic regression analysis is calculated and interpreted similarly to the odds ratios discussed in the Epidemiological Statistics section earlier in the chapter. Briefly, odds ratios range from 0 to infinity, with 1 indicating no association. Therefore, an odds ratio above 1 indicates an increase in the odds of experiencing the primary outcome of interest, whereas an odds ratio below 1 indicates a decrease in the odds of experiencing the primary outcome of interest. For example, reconsider the example above with a slope of −0.25. The associated odds ratio for this slope is 0.78. Because the odds ratio is below 1, a 1% increase in statin use is associated with a 22% (i.e., 1–0.78) decrease in the odds of dying during the study period. It is important to be aware that authors will vary the information they present in journal articles, as one article may only provide slopes as log-odds and another article may provide odds ratios. This is not an issue, however, because there is a direct mathematical relationship between slopes and odds ratios. That is, the slope is the natural log of the odds ratio (i.e., ln 0.78 = −0.25) and the odds ratio is simply the exponentiated slope (i.e., e−0.25 = 0.78). It should become clear that this mathematical relationship is where

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the  definition of log-odds originates; they are literally the log of the odds. Given this relationship, if an author provides log-odds, the odds ratio can be easily calculated to ease interpretation. Also, note that regardless of which value the authors present, the associated p value will be identical. That is, a statistically significant slope will have a statistically significant odds ratio, and vice versa. Finally, similar to linear regression, the primary reason a researcher includes additional IVs and covariates in a multivariable logistic regression model are to increase the amount of variance explained in the primary outcome of interest. That is, multivariable logistic regression models aim to better identify the reason why participants experienced the outcome of interest. However, unlike R2 in linear regression, there is no accepted measure for quantifying explained variance in logistic regression. While a detailed description regarding why is beyond the scope of this chapter, it has to do with the fixed variance of the logistic distribution used to model the residual values. However, be aware that several pseudo-R2 values may be presented in the literature, with the most common including the Nagelkerke R2 and the Cox and Snell R2. These pseudo-R2 values are used to approximate R2 from linear regression and are interpreted in similar fashion. For example, reconsider the 5-year study assessing the effect of statin use on all-cause mortality. Say the Nagelkerke R2 value from the logistic regression analysis was 18%. This value indicates that 18% of the reason why patients died was due to their statin use. Again, it is important to note that these pseudo-R2 values will never be near 100%, and the definition of a large or small pseudo-R2 value is determined by the specific research arena, as discussed in the section on multivariable linear regression. Based on the example described above, the result of a simple logistic regression analysis is presented as follows: The results of a simple logistic regression analysis indicated a statistically significant association between duration of statin use and all-cause mortality. (χ21 = 13.65, p < 0.05) where a 1% increase in duration of statin use resulted in a 22% decrease in the odds of dying during the study period. An example of a multivariable logistic regression analysis, with the addition of age, race, gender, concurrent medications, and comorbid conditions as covariates is presented below. Notice in this example, the researcher is not interested in the individual effects of the covariates, as they are not interpreted. The results of a multivariable logistic regression analysis indicated a statistically significant overall association between the variables as a set and all-cause mortality (χ21 = 156.02, p < 0.05). After controlling for age, race, gender, concurrent medications, and comorbid conditions, duration of statin use significantly predicted all-cause mortality (OR = 0.62, p 50% of patients should have electronic access to their medical information within 36 hours after discharge. The goals of the Meaningful Use program are to (1) improve quality, safety, and efficiency, (2) engage patients and their families, (3) improve care coordination, as well as public and population health, and (4) maintain privacy and security of protected health information (PHI). It is hoped that the adoption of and compliance with Meaningful Use will result in better clinical outcomes, improved population health, increased health care transparency and efficiency, more robust data for research, and the empowerment of individuals and patients.34 Further, there are specific objectives, broken down into three stages, set by CMS that eligible providers and hospitals must achieve to qualify for the incentive program. Stage 1 focuses on providers capturing patient data and sharing that data with either the patient or other health care providers. Stage 2, which begins in 2014 and focuses on advanced clinical processes, requires that a specified number of core and menu objectives be met. Specifically, all of the 15 core objectives must be met, which includes items such as the use of CPOE and e-prescribing, maintaining active drug and allergy lists, checking for drug–drug and drug–allergy interactions, implementation of CDS, and protection of electronic health information. As for the 10 menu objectives, which include items such as submitting electronic data to immunization registries, drug formulary checking, electronic access to health information for

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patients, and patient-specific education resources, five must be met.35 Stage 3, which begins in 2016 and is currently still being formalized, will focus specifically on improved patient outcomes.

Case Study 24–3 You are wrapping up your rotation, and your preceptor comes to you with a final assignment. She has been tasked with representing the Pharmacy Department on the committee that is overseeing the implementation of electronic health records in the institution. She knows very little about EHRs and asks the following questions to get started. What group or organization is overseeing the EHR Meaningful Use program? • Why would a hospital or provider want to start Meaningful Use earlier rather than later? • What are the stages of Meaningful Use? •

Greater Emphasis on Security, Privacy, and Confidentiality of Protected Health Information As interoperability advances and more patient data are shared across providers and organizations, the issues of security, privacy, and confidentiality of protected health information (PHI) inevitability arise. Protected health information includes any information that can be used to identify a person, including information about medical conditions, payment for care, or actual care delivered.1 Privacy refers to being free from unauthorized intrusions and the protection of PHI. In health care organizations, privacy is accomplished through policies that determine how and what information is gathered, stored, and used, as well as how patients are involved in the process. Security here refers to restricting access to patient health data to everyone except those with authorized access. This is accomplished through the use of electronic tools, such as login identifications and password protection. Confidentiality here refers to the provider-client privilege that, under most circumstances, any health-related information communicated between a provider and a patient is private. PHI can be in any form (e.g., written, oral, or electronic), and

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examples include patient name, address, Social Security number, e-mail address, or any other part of a medical record that could be used to identify a patient.1 As health care providers, pharmacists need to be aware of the existing and emerging regulations that apply to the security, privacy, and confidentiality of PHI. For instance, the Health Breach Notification Rule, passed into law in 2009, requires that covered entities provide notice to patients following a breach in security. Also, the Patient Safety and Quality Improvement Act of 2005 (Patient Safety Act) establishes a framework that allows providers to voluntarily and confidentially report patient safety events to patient safety organizations for aggregation and analysis purposes.36

HEALTH INSURANCE PORTABILITY AND ACCOUNTABILITY ACT Another major piece of legislation with which pharmacists and other providers need to be intimately familiar is the Health Insurance Portability and Accountability Act (HIPAA), passed into law in 1996. The main component of concern here is the HIPAA Privacy Rule, which establishes how medical information should be handled. The major goal of the Privacy Rule is to ensure proper protection of individuals’ health information while also allowing the flow of health information needed to provide high-quality health care. As such, the law defines PHI, describes how health care organizations can use and disclose PHI, and outlines the requirements of health organizations to protect PHI from inappropriate disclosure and misuse. For instance, the Privacy Rule specifies that PHI can be transmitted and used for treatment, payment, and operations (TPO) processes (i.e., general health care operational use). And, as it is not intended to prevent providers from discussing the treatment of their patients, the rule specifies that some incidental disclosures of PHI are permitted. That said, reasonable safeguards, such as speaking in a low voice, not discussing patient care in the presence of others, and closing patient charts after use, should be used to limit such incidental disclosures. There are also certain disclosures, permitted by law, which may be made to governmental agencies for such purposes as law enforcement, research, workers’ compensation, and organ donation. Further, the HIPAA Privacy Rule requires that health care organizations notify patients of their rights when receiving care and also provides them with a process to exercise those rights. Also, patients should be given the opportunity to agree or object to disclosures of their PHI while under the care of a health care organization.1,37 There is also the HIPAA Security Rule, which covers electronic PHI (ePHI). ePHI is simply PHI stored in electronic form. Health care organizations are required by the Security Rule to ensure the integrity, confidentiality, and availability of all ePHI created, received, maintained, or transmitted by the organization. As such, most all organizations have fax, Internet, and e-mail policies that outline appropriate use in order to protect ePHI.38

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Conclusion Pharmacy informatics is the scientific discipline found at the intersection of people, data, and technology systems. It is governed by business, technical, and regulatory standards to ensure optimal use of patient-specific and knowledge-based information. Pharmacists rely on informatics to support all of their activities associated with the medication use process. As EHR adoption increases in the United States, pharmacists and other health care providers will continue to see a growing role of informatics in their practice, regardless of the setting.

Self-Assessment Questions 1. Which of the following represents the correct order of the medication use process? a. Monitoring, prescribing, transcribing, administration, follow-up b. Prescribing, transcribing, dispensing, administration, monitoring c. Transcribing, dispensing, administration, monitoring, documentation d. Assessment, prescribing, transcribing, administration, monitoring e. None of the above 2. Pharmacy informatics includes: a. People b. Information c. Technology d. a and b e. a, b, and c 3. The two primary types of information used in pharmacy informatics include: a. Patient specific and clinical expertise b. Clinical expertise and knowledge based c. Patient specific and knowledge based d. Referential and guidelines e. None of the above 4. The primary similarity between CPOE and e-prescriptions is they: a. Are easy to implement because they do not disrupt workflow b. Both address the dispensing stage of the medication use process

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c. Eliminate illegible prescriptions d. Eliminate all errors related to prescribing e. c and d 5. The role of Surescripts is to: a. Certify controlled substance schedule II prescriptions for authenticity b. Provide the communication network for e-prescriptions c. Create standards for CPOE transmission d. Define Meaningful Use standards e. Provide financial support for e-prescription adoption 6. The three components of clinical decision support systems include: a. CPOE system, transmission network, transmission standards b. Knowledge base, e-prescription network, transmission standards c. Communication mechanism, knowledge base, CPOE system d. Inference engine, knowledge base, communication mechanism e. None of the above 7. The main benefits of CDSS include the ability to decrease: a. Adverse drug events b. Costs c. Length of stay d. All of the above e. a and b 8. Prescriptions created electronically (by CPOE and e-prescribing) have already undergone clinical review by CDSS when they reach the pharmacists for transcription. Research suggests that: a. These prescriptions are ready for dispensing, requiring no additional review b. Problems still exist with prescriptions created electronically c. Pharmacists must remain diligent when reviewing all prescription orders d. Physicians do not rely on the information presented to them by CDSS e. b and c 9. The NDC is necessary to support automation of the medication use process. Challenges with use of the NDC include: a. There is no single repository of NDCs b. The FDA does not actually approve NDCs c. NDCs are too long for some automation to read d. a and b e. All of the above

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10. Dispensing automation commonly found in hospitals include: a. Carousel cabinets b. Automated dispensing cabinets c. Robotic cart filling systems d. Sterile compounding devices e. All of the above 11. The core technology supporting pharmacy operations in the community setting is: a. Pharmacy information management system b. Robotic vial filling systems c. Interactive voice response d. Electronic signature capture e. Document scanning systems 12. The three categories of data making up clinical surveillance systems are: a. Radiology, diagnoses, pharmacy b. Patient demographics, pharmacy, laboratory c. Laboratory, radiology, patient demographics d. Diagnoses, patient demographics, radiology e. Diagnoses, pharmacy, nutrition 13. Which of the following is an example of Health 2.0? a. Personal health records b. PatientsLikeMe.com c. Promoting medication adherence through Facebook d. Sending health messages through Twitter e. All of the above 14. The ultimate, desired result of interoperability is: a. The provision of a patient’s complete care history to all providers b. More accurate and efficient billing for health care services c. Streamlined prescription routing from institutional clinics to community pharmacies d. Improved clinical decision making though access to knowledge-based information e. All of the above 15. Goals of Meaningful Use of EHRs include: a. Improve quality of health care b. Engage patients in their own care

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c. Improve coordination of care d. Maintain privacy and security of protected health information e. All of the above

REFERENCES 1. Fox BI, Thrower MR, Felkey BG, eds. Building core competencies in pharmacy informatics. Washington, DC: American Pharmacists Association; 2010. 2. Hersh W. A stimulus to define informatics and health information technology. BMC Med Inform Decis Mak. 2009;9(1):24. 3. Hersh W. Medical Informatics—improving health care through information. JAMA. 2002;288:1955-8. 4. Kohn LT, Corrigan JM, Donaldson MS, eds. To err is human: building a safer health system. Washington, DC: National Academies of Science 2000:1-5. 5. Bates DW, Cullen DJ, Laird N, et al. Incidence of adverse drug events and potential adverse drug events. Implications for prevention. ADE Prevention Study Group. JAMA. 1995;274(1):29-34. 6. Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch Intern Med. 2003;163(12);1409-16. 7. Bates DW, Leape LL Cullen DJ, et al. Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. JAMA. 1998;280(5):1311-6. 8. Koppel R, Metlay JP, Cohen A, et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA. 2005;293(10):1197-203. 9. Hillestad R, Bigelow JH. Health information technology. Can HIT lower costs and improve quality? [Internet]. California: RAND Corporation; 2005 [cited 2012 Dec 28]. Available from: http://www.rand.org/pubs/research_briefs/RB9136.html. Accessed on December 28, 2012. 10. Poon EG, Blumenthal D, Jaggi T, et al. Overcoming barriers to adopting and implementing computerized physician order entry systems in U.S. hospitals. Health Aff. 2004;23(4):184-90. 11. National Council for Prescription Drug Programs [Internet]. Scottsdale (AZ): National Council for Prescription Drug Programs; About—Contact Us; [cited 2012 Dec 28]:[about 1 screen]. Available from: http://www.ncpdp.org/About-Us 12. Centers for Medicare and Medicaid Services. Electronic Prescribing (eRx) Incentive Program [Internet]. Baltimore (MD): Centers for Medicare and Medicaid Services; 2013 May 28 [cited 2012 Dec 28]:[about 3 screens]. Available from: http://www.cms.hhs.gov/ ERxIncentive 13. U.S. Department of Justice Drug Enforcement Administration, Office of Diversion Control. Notices—2012 [Internet]. Springfield (VA): Drug Enforcement Administration; 2012 Aug 1 [cited 2012 Dec 28]:[about 3 screens]. Available from: http://www.deadiversion .usdoj.gov/fed_regs/notices/2012/fr0801_4.htm

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14. Osheroff JA, Teich JM, Middleton B, et al. A roadmap for national action on clinical decision support. J Am Med Inform Assoc. 2007;14(2):141-5. 15. Shea S, DuMouchel W, Bahamonde L. A meta-analysis of 16 randomized controlled trials to evaluate computer-based clinical reminder systems for preventive care in the ambulatory setting. J Am Med Inform Assoc. 1996;3(6):399-409. 16. Berner ES. Clinical decision support systems: theory and practice. 2nd ed. New York, NY: Springer; 2007. 17. Overhage JM, Tierney WM, Zhou XH, et al. A randomized trial of “corollary orders” to prevent errors of omission. J Am Med Inform Assoc. 1997;4(5):364-75. 18. Koppel R, Metlay JP, Cohen A, et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA. 2005;293(10):1197-1203. 19. Saverno KR, Hines LE, Warholak TL, et al. Ability of pharmacy clinical decision-support software to alert users about clinically important drug–drug interactions. J Am Med Inform Assoc. 2011;18(1):32-37. 20. Oren E, Griffiths LP, Guglielmo BJ. Characteristics of antimicrobial overrides associated with automated dispensing machines. Am J Health Syst Pharm. 2002;59(15);1445-8. 21. Kester K, Baxter J, Freudenthal K. Errors associated with medications removed from automated dispensing machines using override function. Hosp Pharm. 2006;41:53-537. 22. Class II special controls guidance document: pharmacy compounding systems; final guidance for industry and FDA [Internet]. Rockville (MD): U.S. Department of Health and Human Services, Food and Drug Administration, Centers for Devices and Radiological Health; 2001 Mar 12 [cited 2012 Dec 29]:[9 p.]. Available from: http://www.fda.gov/ downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ ucm073589.pdf 23. Pharmacy e-Health Information Technology Collaborative Home page [Internet]. Alexandria (VA): Pharmacy e-Health Information Technology Collaborative; c2011 [cited 2012 Dec 29]. Available from: http://www.pharmacyhit.org/. 24. Smith R. Information technology and consumerism will transform health care worldwide. BMJ. 1997;314:1495. 25. Wikipedia: The Free Encyclopedia [Internet]. Wikipedia; [cited 20112 Dec 29]. Available from: http://en.wikipedia.org/wiki/Main_Page 26. AHIMA e-HIM Personal Health Record Work Group. The role of the personal health record in the EHR. J AHIMA. 2005;76(7):64A-D. 27. eHealth Initiative. Migrating toward Meaningful Use: the state of health information Exchange. Washington, DC: eHealth Initiative; August 2009. Available from: http://www .sftvision.com/2009SurveyReportFINAL.pdf 28. Walker J, Pan E, Johnson D, et al. The value of health care information exchange and interoperability [Internet]. Bethesda (MD): Health Affairs; 2005 Jan 19 [cited 2012 Dec 29]:[8p.]. Available from: http://content.healthaffairs.org/content/suppl/2005/02/07/ hlthaff.w5.10.DC1 29. The National Alliance for Health Information Technology report to the Office of the National Coordinator for Health Information Technology on defining key health

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30.

31.

32.

33.

34.

35.

36.

37.

38.

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information technology terms [Internet]. Department of Health and Human Services; 2008 Apr 28 [cited 2012 Dec 29]. 40 p. Available from: http://cdm16064.contentdm.oclc .org/cdm/singleitem/collection/p266901coll4/id/2086/rec/10 U.S. National Library of Medicine [Internet]. Bethesda (MD): U.S. National Library of Medicine; 1993 Oct 10 [updated 2013 Jul 3]. RxNorm overview; 2005 May 5 [updated 2013 Mar 5; cited 2012 Dec 29]:[about 14 screens]. Available from: https://www.nlm.nih.gov/ research/umls/rxnorm/overview.html U.S. National Library of Medicine [Internet]. Bethesda (MD): U.S. National Library of Medicine; 1993 Oct 10 [updated 2013 Jul 3]. UMLS quick start guide; 2011 Mar 30 [updated 2012 Jul 27; cited 2012 Dec 29]:[about 2 screens]. Available from: http://www .nlm.nih.gov/research/umls/quickstart.html American Medical Association. About CPT® [Internet]. Chicago (IL): American Medical Association; c1995-2013 [cited 2012 Dec 29]:[about 1 screen]. Available from: http:// www.ama-assn.org/ama/pub/physician-resources/solutions-managing-your-practice/ coding-billing-insurance/cpt/about-cpt.page Bush GW. Executive Order 13335—incentives for the use of health information technology and establishing the position of the National Health Information Technology Coordinator [Internet]. Washington, DC: Federal Register; 2004 Apr 27 [cited 2012 Dec 29]:[3 p.]. Available from: http://www.gpo.gov/fdsys/pkg/FR-2004-04-30/pdf/04-10024.pdf HealthIT.gov. Meaningful use definitions and objectives [Internet]. Washington, DC: The Office of the National Coordinator for Health Information Technology; [cited 2012 Dec 29]:[about 1 screen]. Available from: http://www.healthit.gov/providers-professionals/ meaningful-use-definition-objectives An introduction to the Medicaid EHR Incentive Program for eligible professionals [Internet]. Baltimore (MD): Centers for Medicare and Medicaid Services; [cited 2012 Dec 29]:[94 p.]. Available from: http://www.cms.gov/Regulations-and-Guidance/Legislation/ EHRIncentivePrograms/Downloads/EHR_Medicaid_Guide_Remediated_2012.pdf Summary of selected federal laws and regulations addressing confidentiality, privacy and security [Internet]. Washington, DC: The Office of the National Coordinator for Health Information Technology; 2010 Feb 18 [cited 2012 Dec 29]:[12 p.]. Available from: http:// www.healthit.gov/sites/default/files/federal_privacy_laws_table_2_26_10_final_0.pdf Summary of the HIPAA Privacy Rule [Internet]. Washington, DC: U.S. Department of Health and Human Services; 2005 May [cited 2012 Dec 29]:[25 p.]. Available from: http:// www.hhs.gov/ocr/privacy/hipaa/understanding/summary/privacysummary.pdf HIPAA administrative simplification [Internet]. Washington, DC: U.S. Department of Health and Human Services; 2006 Feb 16 [cited 2012 Dec 29]:[101 p.]. Available from: http://www .hhs.gov/ocr/privacy/hipaa/administrative/privacyrule/adminsimpregtext.pdf

SUGGESTED READINGS 1. Fox BI, Thrower MR, Felkey BG, eds. Building core competencies in pharmacy informatics. Washington, DC: American Pharmacists Association; 2010.

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2. The Office of the National Coordinator for Health Information Technology [Internet]. Washington, DC: U.S. Department of Health and Human Services; [updated 2011 Feb 18; cited 2012 Dec 12]. Available from: http://healthit.hhs.gov 3. Journal of the American Medical Informatics Association, http://jamia.bmj.com/. 4. Friedman CP. A ‘fundamental theorem’ of biomedical informatics. JAMIA. 2009;16:169-70. 5. Pew Internet and American Life Project, http://www.pewinternet.org/. 6. American Society of Health-System Pharmacists. Technology-enabled practice: vision statement by the ASHP Section of Pharmacy Informatics and Technology. Am J Health Syst Pharm. 2009;66:1573-77. 7. Journal of Medical Internet Research, http://www.jmir.org 8. Journal of Participatory Medicine, http://www.jopm.org 9. Journal of Biomedical Informatics, http://www.sciencedirect.com/science/journal/ 15320464 10. Pharmacy Informatics, http://www.pharmacy-informatics.com 11. Dumitru D, ed. The pharmacy informatics primer. Bethesda (MD): American Society of Health-System Pharmacists; 2009.

Appendices

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2-1 Appendix 2–1

Drug Consultation Request Form

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2-2 Appendix 2–2

Examples of Questions for Obtaining Background Information from Requestors* Regardless of the type or classification of the question, the following information should be obtained: 1. 2. 3. 4. 5. 6. 7. 8.

The requestor’s name The requestor’s location and/or page number The requestor’s affiliation (institution or practice), if a health care professional The requestor’s frame of reference (e.g., title, profession/occupation, rank) The resources the requestor has already consulted If the request is patient-specific or academic The patient’s diagnosis and other medications The urgency of the request (negotiate time of response)

The following are examples of questions that can be asked to clarify the initial query and elicit pertinent background information. Please note that this Appendix addresses selected categories of queries and lists only a sampling of questions that can be posed to the requestor (i.e., it is not a comprehensive list).

Availability of Dosage Forms 1. 2. 3. 4. 5.

What is the dosage form desired? What administration routes are feasible with this patient? Is this patient alert and oriented? Does the patient have a water or sodium restriction? What other special factors regarding drug administration should be considered?

Identification of Product 1. 2. 3. 4. 5. 6.

∗

What is the generic or trade name of the product? Who is the manufacturer? What is the country of origin? What is the suspected use of this product? Under what circumstances was this product found? Who found the product? What is the dosage form, color markings, size, etc.? What was your source of information? Was it reliable?

Originally prepared by Craig Kirkwood

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General Product Information 1. 2. 3. 4.

Why is there a particular concern for this product? Is written patient information required? What type of information do you need? Is this for an inpatient, outpatient, or private patient?

Foreign Drug Identification 1. What is the drug’s generic name, trade, manufacturer, and/or country of origin? 2. What is the dosage form, markings, color, strength, or size? 3. What is the suspected use of the drug? How often is the patient taking it? What is the patient’s response to the drug? Is the patient male or female? 4. If the medication was found, what were the circumstances/conditions at the time of discovery? 5. Is the patient just visiting, or are they planning on staying?

Investigational Drug Information 1. Why do you need this information? Is the patient in need of the drug or currently enrolled in a protocol? 2. If a drug is to be identified, what is the dosage form, markings, color, strength, or size of the product? 3. Why was the patient receiving the drug? What is the response when the patient was on the drug? What are the patient’s pathological conditions? 4. If a drug is desired what approved or accepted therapies have been tried? Was therapy maximized before discontinued?

Method and Rate of Administration 1. What dosage form or preparation is being used (if multiple salts available)? 2. What is the dose ordered? Is the drug a one-time dose or standing orders? 3. What is the clinical status of the patient? For example, could the patient tolerate a fluid push of [ ] mL? Is the patient fluid or sodium restricted? Does the patient have CHF or edema? 4. What possible delivery routes are available? 5. What other medications is the patient receiving currently? Are any by the same route?

Incompatibility and Stability 1. 2. 3. 4. 5. 6.

What are the routes for the patient’s medications? What are the doses, concentrations, and volumes for all pertinent medications? What are the infusion times/rates expected or desired? What is the base solution or diluent used? Was the product stored and handled appropriately, based on requirements? When was the product compounded/prepared?

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Drug Interactions 1. What event(s) suggest that an interaction occurred? Describe. 2. For the drugs in question, what are the doses, volumes, concentrations, rate of administration, administration schedules, and length of therapies? 3. What is the temporal relationship between the drugs in question? 4. Has the patient received this combination or a similar combination in the past? 5. Other than the drugs in question, what other drugs is the patient receiving currently? When were these started?

Drug-Laboratory Test Interference 1. What event(s) suggest an interaction occurred? Describe. 2. For the drug in question, what is the dose, volume, concentration, rate of administration, administration schedule, and length of therapy? 3. What is the temporal relationship between drug administration and laboratory test sampling? 4. What other drugs is the patient receiving? 5. Has clinical chemistry (or the appropriate laboratory) been contacted? Are they aware of any known interference similar to this event? 6. Was this one isolated test or a trend in results?

Pharmacokinetics 1. 2. 3. 4. 5. 6. 7. 8.

Which product (e.g., drug, dose, brand) is being used? What is the dose and route of the drug? What is the patient’s age, sex, height, and weight? What are the disease being treated and the severity of the illness? What is the patient’s hepatic and renal function? What other medications is the patient receiving? What physiologic conditions exist (e.g., pneumonia, severe burns, or obesity)? What are the patient’s dietary and ethanol habits?

Therapeutic Levels 1. Is the patient currently receiving the drug? Have samples already been drawn? At what time? 2. What is the disease or underlying pathology being treated? If infectious in nature, what is the organism suspected/cultured? 3. If not stated in the question, what was the source of the sample (blood, urine, saliva; venous or arterial blood)? 4. What was the timing of the samples relative to drug administration? Over what period of time was the drug administered and by what route? 5. What were the previous concentrations for this patient? Was the patient receiving the same dose then? 6. How long has the patient received the drug? Is the patient at steady state?

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Therapy Evaluation/Drug of Choice 1. 2. 3. 4. 5. 6.

What medications, including doses and routes of administration, is the patient receiving? What are the patient’s pathology(ies) and disease(s) severity? What are the patient’s specifics: age, weight, height, gender, organ function/dysfunction? Has the patient received the drug previously? Was the response similar? Has the patient been adherent? What alternative therapies has the patient received? Was therapy maximized for each of these before discontinuation? What other therapies are being considered? 7. What monitoring parameters have been followed (serum concentrations/levels, clinical status, other clinical lab results, objective measurements, and subjective assessment)?

Dosage Recommendations 1. 2. 3. 4. 5. 6. 7. 8.

What disease is being treated? What is the extent/severity of the illness? What are the medications (all) being prescribed? Has the patient been adherent? Does the patient have any insufficiency of the renal, hepatic, or cardiac system? For drugs with renal elimination, what are the serum creatinine/creatinine clearance, blood urea nitrogen (BUN), and/or urine output? Is the patient receiving peritoneal dialysis or hemodialysis? For drugs with hepatic elimination, what are the liver function tests (LFTs), bilirubin (direct and indirect), and/or albumin? For drugs with serum level monitoring utility, characterize the most recent levels per timing relative to dose and results. Are these lab values recent? Is the patient’s condition stable? Does this patient have a known factor that could affect drug metabolism (ethnic background or acetylator status)?

Adverse Events 1. What are the name, dosage, and route for all drugs currently and recently prescribed? 2. What are the patient specifics (age, sex, height, weight, organ dysfunction, and indication for drug use)? 3. What is the temporal relationship with the drug? 4. Has the patient experienced this adverse relationship (or a similar event) with this drug (or similar agent) previously? 5. Was the suspected drug ever administered before? Why was it discontinued then? 6. What were the events/findings that characterize this adverse drug reaction (include onset and duration)? 7. Has any intervention been initiated at this time? 8. Does the patient have any food intolerance? 9. Is there a family history for this ADR and/or drug allergy?

APPENDIX 22

1081

Toxicology Information 1. What is your name, relationship to the patient, and telephone number? 2. What are the patient specifics (age, sex, height, weight, organ dysfunction, and indication for drug use)? 3. Is this a suspected ingestion or exposure? 4. What is the product suspected to have been ingested? What is the strength of the product and the possible quantity ingested (e.g., how much was in the bottle)? 5. How long ago did the ingestion occur? 6. How much is on the patient or surrounding area? 7. How much was removed from the patient’s hands and mouth? Was the ingestion in the same room where the product was stored? 8. What has been done for the patient already? Has the poison control center or emergency department been called? 9. Do you have syrup of ipecac available (only if recommended by a poison control center)? Do you know how to give it properly? 10. What is the patient’s condition (sensorium, heart rate, respiratory rate, temperature, skin color/turgor, pupils, sweating/salvation, etc.)? 11. Does the patient have any known illnesses?

Teratogenicity/Drugs in Pregnancy 1. What is the drug the patient received and what was the dose? What was the duration of therapy? 2. Is the patient pregnant or planning to become pregnant? 3. When during pregnancy was the exposure (trimester or weeks)? 4. What are the patient specifics (age, height, weight, sex)? 5. Was the patient adherent? 6. For what indication was the drug being prescribed?

Drugs in Breast Milk/Lactation 1. What is the drug the patient received and what was the dose? What was the duration of therapy? 2. How long has the infant been breast-feeding? 3. Has the infant ever received nonmaternal nutrition? Is bottle feeding a plausible alternative? 4. What is the frequency of the breast feeds? What is the milk volume? 5. How old is the infant? 6. Does the mother have hepatic or renal insufficiency? 7. What was the indication for prescribing the drug? Was this initial or alternate therapy? 8. Has the mother breast-fed previously while on the drug?

3-1 Appendix 3–1

Performing a PubMed Search PubMed Search PubMed is a database which is maintained by the National Library of Medicine and is available to the public at no charge. This database is available online at http://www.ncbi.nlm.nih.gov/PubMed. The information indexed by PubMed includes Medline, OldMedline (articles from the 1950s to the mid1960s), as well as citations for additional life science journals. This database is especially helpful when looking for off-label uses of medications. For example, if a prescriber contacts you asking for information about the efficacy of fluoxetine in treatment of anorexia nervosa, it may be appropriate to seek information from the primary literature. A PubMed search might be a good place to start this search. When performing a search using PubMed one can begin with just a key word, for example, fluoxetine. As Figure 1 shows, just using the term fluoxetine yields in 10,310 results. The results can be narrowed by entering a second key word, such as anorexia nervosa, and combining the two terms with the Boolean operator AND. While the addition of a second search term (see Figure 2) did narrow the results, there are still 88 results that match these two terms. At this time it may be wise to explore the limit options provided by the database. Limits allow the user to restrict the number of results returned for a search. Some databases allow searches to be limited by a variety of factors, including language of publication, year of publication, type of article (e.g., human study, review, case report), or type of journal where publication is found. Since the requestor is seeking efficacy data, it is appropriate to limit these search results to just clinical trials. By limiting the results to only human clinical trials published in English, 23 citations of possible interest have been identified (Figure 3). It is now necessary to look at the abstracts for these citations (Figure 4) and determine if these are helpful to provide a response to the query. By clicking on the blue hyperlink an abstract is displayed; this abstract summarizes the information in the article, as well as provides complete citation information for that specific article. If the publisher’s Web site offers full text of an article, a link is provided at the top of the page to the journal Web site. Some journals charge a fee for access to the full-text article while others do not. Those journals not charging for an article are clearly marked as “free full text.” You can then select that icon and go directly to a full-text PDF or html of the desired article. One additional helpful feature offered by PubMed is the “Related citations” link. The database will first identify the key words or Medical Subject Headings (MeSH) associated with the article selected and then identify secondary words and terms. The database will then compare these terms

1082

APPENDIX 31

1083

(both primary and secondary terms) with other articles indexed in PubMed to determine which other articles include similarly ranked terms and, therefore, might be of interest. The best way to effectively search this database is by experience. However, PubMed offers a tutorial to gain additional experience in how to most effectively conduct literature searches. This interactive tutorial session is available at http://www.nlm.nih.gov/bsd/disted/pubmedtutorial/.

Number of results using just one keyword

Figure 1. Key word search.

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

Easy filters to limit search results

Number of citations with both terms

Terms used with Boolean operator

Figure 2. Multiple key word search.

Restrictions or limits used

Number of citations matching search criteria with restrictions

Figure 3. Results of search with restrictions.

APPENDIX 31

Synopsis of article information.

Link to full text article

Citation information – journal, year, volume (issue): pages. Specific number assigned to each article by PubMed, may be helpful to order articles

Figure 4. PubMed abstract.

Related articles

1085

3-2 Appendix 3–2

Selected Primary Literatures Sources

Journal Title

Publisher

ISSN

Areas Covered

American Journal of Health-System Pharmacy: AJHP

American Society of Health-System Pharmacy

1079-2082

Clinical and managerial areas of pharmacy practice in health systems

American Journal of Pharmacy Education

American Association of Colleges of Pharmacy

0002-9459

Scholarship and advancement of pharmacy education

Annals of Internal Medicine

American College of Physicians

0003-4819

Internal medicine, including management of disease states

Annals of Pharmacotherapy

Sage Journals

1060-0280

Safe, effective, and economical use of drugs

Antimicrobial Agents and Chemotherapy

American Society for Microbiology

0066-4804

Information regarding the use of antimicrobial agents

Archives of Internal Medicine

American Medical Association

0003-9926

Focus on the diagnosis and treatment of disease states

Clinical Pharmacokinetics

Adis International Limited

0312-5963

Focus on pharmacokinetic and pharmacodynamic properties of drugs

Clinical Pharmacology and Nature Publishing Group Therapeutics

0009-9236

The effect of drugs on the human body

Drug Information Journal

Drug Information Association

0092-8615

Technology related to disseminating drug information

Drug Topics

Thomson Healthcare

0012-6616

Focus on issues impacting community pharmacy and on new drug therapies

Drugs

Adis International Limited

0012-6667

Pharmacotherapeutic aspects of both new and established drugs

Formulary

Advanstar Communications Incorporated

1082-801X

Contemporary issues in drug policy management and pharmacotherapy continued

1086

1087

APPENDIX 32

Journal Title

Publisher

ISSN

Areas Covered

Hospital Pharmacy

Thomas Land Publishers Incorporated

0018-5787

Issues related to pharmacy in institutional settings

JAMA (the Journal of the American Medical Association)

American Medical Association

0098-7484

New research and review information that impacts health care

Journal of Cardiovascular Pharmacology

Lippincott Williams & Wilkins

0160-2446

New information about the treatment of cardiovascular disease.

The Journal of Clinical Pharmacology

Lippincott, Williams & Wilkins

0091-2700

Clinical information about the safety, tolerability, efficacy, therapeutic use, and toxicology of drugs

Journal of Pharmaceutical Sciences

Wiley

0022-3549

Application of physical and analytical chemistry to pharmaceutical sciences

Journal of Pharmacology and Experimental Therapeutics

American Society for Experimental Pharmacology and Therapeutics

0022-3565

Covers interaction between chemicals and biological systems as well as metabolism, distribution, and toxicology.

Journal of Pharmacy and Pharmacology

Pharmaceutical Press

0022-3573

Addresses a variety of practice areas including drug delivery systems, biomaterials and polymers, and implications of human genome on drug therapies.

Journal of the American Pharmacists Association

American Pharmacists Association

1544-3191

News, information, and research in the area of pharmacotherapeutic management

Medical Letter on Drugs and Therapeutics

Medical Letter, Inc.

0025-732X

Provides information on new drug therapies and drugs of choice for disease management

New England Journal of Medicine

Massachusetts Medical Society

0028-646X

Results of recent research considered important to the practice of medicine

Pharmaceutical Research

Plenum Press

0724-8741

Emphasis on drug delivery, drug formulation, pharmacokinetics, pharmacodynamics, and drug disposition

PharmacoEconomics

Adis International Limited

1170-7690

Information regarding the economical use of drug therapies

Pharmacological Reviews

American Society of 0031-6997 Pharmacology and Experimental Therapeutics

Current topics of interest including cellular pharmacology, drug metabolism and disposition, renal pharmacology, and neuropharmacology continued

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

Journal Title

Publisher

ISSN

Areas Covered

Pharmacotherapy

IOS Press

0277-0008

Published by American College of Clinical Pharmacy and focused on original research in clinical practice

Pharmacy Times

Romaine Pierson Publishing Incorporated

0003-0627

Focus on new drug therapies and patient counseling as it relates to community pharmacy

Therapeutic Drug Monitoring

Lippincott Williams & Wilkins

0163-4356

Fosters exchange of knowledge between fields of pharmacology, pathology, toxicology, and analytical.

U.S. Pharmacist

Jobson Publishing Corporation

0148-4818

Information regarding the practice of community pharmacy

4-1 Appendix 4–1

Questions for Assessing Clinical Trials OVERALL ASSESSMENT • Was the article published in a reputable, peer-reviewed journal? • Are the investigator’s training/education/practice sites adequate for the study objective? • Can the funding source bias the study?

TITLE/ABSTRACT • Was the title unbiased? • Did the abstract contain information not found within the study? • Did the abstract provide a clear over view of the purpose, methods, results, and conclusions

of the study?

INTRODUCTION • Did the authors provide sufficient background information to demonstrate the rationale for

the study? • Were the study objectives clearly identified? • What were the major null hypothesis and alternate hypothesis?

METHODS • Was an appropriate study design used to answer the question? • Were reasonable inclusion/exclusion criteria presented to represent an appropriate patient

population? • Was a selection bias present? • Was subject recruitment described? If so, how were subjects recruited? Was the method

appropriate? • Was IRB approval obtained? • Was subject informed consent obtained? • Were the intervention and control regimens appropriate? • What type of blinding was used? Was this type appropriate? • Was randomization included? If so, what type was used? Was this appropriate? • Who generated the allocation sequence, enrolled participants, and assigned participants to

groups? Was this appropriate?

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

• Which ancillary treatments were permitted? Would they have affected the outcome? • Was a run-in period included? How does this affect the results? • Did the investigators measure compliance? How was compliance measured? Was compliance

adequate? • Was the primary endpoint appropriate for the study objective? • Were secondary endpoints measured? If so, were they adequate for what was being • • • • •

• •

studied? Were planned subgroup analyses planned? If so, were they appropriate? Was the method used to measure the primary endpoint appropriate? What type of data best describes the primary endpoint? Were data collected appropriately? What number of patients was needed for the primary endpoint to detect a difference between groups (power analysis)? Was the necessary sample size calculated? Were there enough patients enrolled to reach this endpoint? What were the alpha (α) and beta (β) values? Were these appropriate? Were the statistical tests used appropriate?

RESULTS • Were the numbers of patients screened, enrolled, administered treatment, completing, and

• • • • • • • • • • • • •

withdrawing from the study reported? Were reasons for subject discontinuations reported? Were withdrawals handled appropriately? Was the trial adequately powered? Were the subject demographics between groups similar at baseline? If not, were the differences likely to have an affect on the outcome data? Were data presented clearly? Were the results adjusted to take into account confounding variables? Was intention-to-treat analysis used? Was this appropriate? Were estimated effect size, p-values, and confidence intervals reported? Were the results statistically significant? Clinically different? Was the null hypothesis accepted or rejected? Can the trial results be extrapolated to the population? Based on the results, could a Type I or Type II error have occurred? Are subgroup analysis presented? Are these appropriate? Was ancillary therapy included? Did this affect the study results? Were therapy adverse effects included?

CONCLUSIONS/DISCUSSION • Did the information appear biased, and did the trial results support the conclusions? • Were trial limitations described? • Did the investigators explain unexpected results?

APPENDIX 41

1091

• Are the results able to be extrapolated to the population? • Were the study results clinically meaningful?

REFERENCES • Were the references listed well represented (e.g., current, well-representing the literature)? • Is a comprehensive list of published articles related to the trial objective presented?

5-1 Appendix 5–1

Beyond the Basics: Questions to Consider for Critique of Primary Literature RANDOMIZED CONTROLLED TRIAL • Refer to Chapter 4, Drug Literature Evaluation I: Controlled Clinical Trial Evaluation

PHARMACOECONOMIC ANALYSIS • Refer to Chapter 6, Pharmacoeconomics

NONINFERIORITY TRIAL • Is the reference drug’s efficacy established using adequate historical data and/or addition of •

•

• •

•

a placebo arm? Are the participants and outcome measures for the noninferiority similar compared to previous studies that confirmed the efficacy of the reference drug (e.g., constancy assumption)? Is the method used to determine the noninferiority (NI) margin predetermined before the study, both clinically and statistically sound, and the reasoning clearly stated in the article? Was a per-protocol analysis used? If so, did they also perform an intention-to-treat analysis? Was the sample size modified as the study progressed? If so, was a clear explanation of how the blinded information was handled and by what method these modifications were determined also provided? Was this study an attempt to rescue a failed superiority trial?

NOF1 TRIAL • Was assignment of active and control treatment to study periods randomized? • Was the study blinded? • Were multiple observation periods used? • Were study endpoints clearly defined? • Was the washout period between study periods adequate?

1092

APPENDIX 51

1093

ADAPTIVE CLINICAL TRIAL ACT • Are the methodologies used to make adaptive changes in the trial adequately described? • What logistical issues exist and have they been adequately addressed? • Are the adaptive changes based on evidence and good clinical judgment? • Did extensive adaptation to the protocol occur during the study? • To what extent is intrusion of bias noted? • Who has access to the information created from interim analyses? • If the study was stopped early, is the totality of evidence adequate?

STABILITY STUDY • Were study methodologies and test conditions clearly defined? • Were validated assays used? • Were assays validated using time-zero measurements and an adequate number of test

samples taken?

BIOEQUIVALENCY STUDY • Did the protocol define the characteristics of the subjects? • Were confounding factors (e.g., smoking, alcohol use) identified and controlled? • Was a crossover design used? • Was the study randomized and blinded?

PROGRAMMATIC RESEARCH • Were one of two options used for subject comparison: (1) comparison of subjects to those not

• • •

•

using the program or service, (2) comparison of subjects before or after initiation of the program or service? Was the program or service clearly defined? Did the authors specify from whose perspective (e.g., patient, provider, physician, third-party payer) the study was undertaken? If costs were analyzed, were all costs associated with provision of the program or service included in the analysis, including personnel, inflationary changes, and cost savings had the intervention not occurred? Were clinically important outcome parameters used to assess effectiveness of the program or service?

COHORT STUDY • Was the research question clearly stated? • Were inclusion and exclusion criteria described in detail? • Were exposed and unexposed subjects similar in terms of demographic characteristics and

susceptibility to disease states? • Was selection bias obviously present?

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

• Were confounding factors obviously present? • Were the same efforts to measure outcomes made in each group? • Were 95% confidence intervals calculated? • Were follow-up rates the same for the exposed and unexposed groups?

CASECONTROL STUDY • Was predisposition of disease similar in cases and controls except for exposure to the risk

factor? • Were cases and controls matched? • Was exposure to the risk factor similar to that which would occur in the general population? • Did cases and controls undergo similar diagnostic evaluations? • Were investigators who assessed patients or collected data blinded to the status of the subject

as a case or control? • Did the investigators compare cases with several different control groups? • Were 95% confidence intervals calculated?

CROSSSECTIONAL STUDY • Did investigators ensure accuracy in data collection? • If a survey or questionnaire was used, was it validated? • Were the inclusion and exclusion criteria clearly defined and stated? • Was selection of cases clearly described?

CASE STUDY, CASE REPORT, OR CASE SERIES • Did the authors recognize the preliminary nature of the results (i.e., recommendations for

clinical application of the results should be guarded)?

SURVEY STUDY • Was the survey instrument valid and reliable? Was a pretest or pilot test conducted on the

survey instrument? • Was the sample size large enough to detect a difference between groups? • Was the survey objective and carefully planned? • Were data quantifiable? • Was the sample representative of the target population? • Was response rate high enough to reflect results that would be expected of the target population? • Did the investigators determine whether nonresponders differed from responders?

POSTMARKETING SURVEILLANCE STUDY • Was a large enough sample studied to reflect current uses of and side effects associated with

the new drug therapy? • Were appropriate methods used to measure clearly defined endpoints?

APPENDIX 51

1095

NARRATIVE NONSYSTEMATIC REVIEWSQUALITATIVE • Was an extensive search for available studies undertaken? • Did the authors use a variety of resources to identify studies for inclusion in the review

article? • Was the review article focused on a clearly defined population? • Did the studies included in the review article use valid research methods? • Did the author examine reasons for differences in study results and conclusions? • Were outcomes of the studies clinically important? • Did the author consider benefits and risks of the drug therapy?

SYSTEMATIC REVIEWSQUALITATIVE • Did the authors clearly define the research question? • Was the review article focused on a clearly defined patient population? • Was an extensive search for available studies undertaken? • Did the authors consider using results from both published and unpublished studies in the

analysis? • Did the authors clearly define criteria for study inclusion in the analysis? • Did the authors list studies that were included in and excluded from the analysis? • Did the authors provide details concerning methodologies of studies used in the analysis? ° Were included studies addressing the same clinical question(s)? ° Did all included studies use appropriate doses, regimens, and routes of administrations for

both treatments and comparators? ° Were all included studies of appropriate duration? • Were those who selected studies for inclusion in the analysis blinded to the names of the original authors, place of publication of the study, and final study results?

METAANALYSISQUANTITATIVE • Did the authors clearly define the research question? • Was the review article focused on a clearly defined patient population? • Was an extensive search for available studies undertaken? • Did the authors consider using results from both published and unpublished studies in the • • • •

analysis? Did the authors clearly define criteria for study inclusion in the analysis? Did the authors list studies that were included in and excluded from the analysis? Did the authors include gray literature in the analysis? Did the authors provide details concerning methodologies of studies used in the analysis? ° Were included studies addressing the same clinical question(s)? ° Did all included studies use appropriate doses, regimens, and routes of administrations for both treatments and comparators? ° Were all included studies of appropriate duration?

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

• Was a funnel plot provided to determine if publication bias was potentially present? • Were tests of homogeneity performed and results reported? • Were those who selected studies for inclusion in the analysis blinded to the names of the

original authors, place of publication of the study, and final study results? • Were appropriate statistical tests used and the probability of Type I and Type II errors

considered? Were 95% confidence intervals calculated? • • Were results presented in a forest plot?

PRACTICE GUIDELINES • Is there an explicit description of the procedures used to identify, select, and combine

evidence? • Are the recommendations valid? • Are the guidelines regularly reviewed and updated to incorporate new evidence as it becomes • • • •

available? Was the guideline peer reviewed? Can the recommendations be generalized to a larger population? Was the source of funding for the development of the guideline provided and could it bias the conclusions? If another group of experts were to independently develop a guideline on the same clinical situation, would the recommendations be the same (are the recommendations reliable)?

QUALITYOFLIFE STUDIES • Are HR-QOL instruments validated? • If a series of HR-QOL measurements are used, does this result in a valid HR-QOL battery? • Are HR-QOL instruments sensitive to changes in the patients’ status as the trial progresses? • Are important aspects of patients’ lives measured, as determined by patients themselves? • Is timing of HR-QOL measurements to answer the research questions appropriately related

to anticipated timing of the clinical effects? • Were sequences of HR-QOL assessments conducted in the same order for all patients? • Was mode of data collection (self-report versus trained interviewer) appropriate for type of

• •

• • •

questions being asked? ° If mode of data collection was a trained interviewer, could the interview location lead to biased answers? Were response rates to questionnaires reported? Is there missing data? ° If missing data exist, is there a specific pattern that suggests author manipulation providing desired results (missing data could have countered author’s hypothesis)? If a multicenter trial, did all sites evaluate HR-QOL? Is the QOL instrument valid for examining the specific disease in question? Are both positive and negative findings reported?

APPENDIX 51

1097

• Are adverse drug events and HR-QOL measurements considered separately? • Is impact of treatment effects included with HR-QOL measurements? • Is there evidence that culturally defined factors may have impacted patient HR-QOL

measurements and/or the assessment of these measurements? • Does there appear to be a bias on the part of the study researchers? • Were general measures of QOL evaluated?

DIETARY SUPPLEMENT MEDICAL LITERATURE • Which plant part was utilized? • Was a standardized botanical extract utilized? • Was the study product standardization appropriate? • Was a specific plant species or specific salt form utilized? • Was the study dose appropriate? • Was trial length appropriate to perceive treatment effects or differences? • Was sample size sufficient to detect a difference between groups if one exists?

7-1 Appendix 7–1

Grade Evidence Profile: Antibiotics for Children with Acute Otitis Media

1098

Quality Assessment

Summary of Findings

Number of studies (Design)

Number of patients Publication bias

Placebo

Antibiotics

Relative risk (95% Cl)

Absolute risk Control riska Risk difference (95% Cl)

Quality

Limitations

Inconsistency

Indirectness

Imprecision

No serious limitations

No serious inconsistency

No serious indirectness

No serious imprecision

Undetected

241/605

223/624

RR 0.9 (0.78-1.04)

367/1000

Not significant

⊕⊕⊕⊕ High

No serious limitations

No serious inconsistency

No serious indirectness

No serious imprecision

Undetected

303/1366

228/1425

RR 0.72 (0.62-0.83)

257/1000

72 fewer per 1000 (44-98)

⊕⊕⊕⊕ High

No serious imprecision

Undetected

168/460

153/467

RR 0.89 (0.75-1.07)

350/1000

Not significant

⊕⊕⊕ο Moderate

Pain at 24h 5 (RCT) Pain at 2-7 d 10 (RCT)

Hearing, inferred from the surrogate outcome abnormal tympanometry—1 mo 4 (RCT)

No serious limitations

No serious inconsistency

Serious indirectness (because of indirectness of outcome)

Hearing, inferred from the surrogate outcome abnormaltympamometry—3 mo 3 (RCT)

No serious limitations

No serious inconsistency (because of indirectness of outcome)

Serious indirectness

No serious imprecision

Undetected

96/398

96/410

RR 0.97 (0.76-1.24)

234/1000

Not significant

⊕⊕⊕ο Moderate

Serious inconsistency (because of inconsistency in absolute effects)

No serious indirectness

No serious imprecision

Undetected

83/711

110/690

RR 1.38 (1.09-1.76)

113/1000

43 more per 1000 (10-86)

⊕⊕⊕ο Moderate

Vomiting, diarrhea, or rash 5 (RCT)

No serious limitations

1099

a The control rate is based on the median control group risk across studies. GRADE = Grading of Recommendations Assessment, Development, and Evaluation; RCT = randomized controlled trials; CI = confidence interval; RR = risk ratio. Reproduced from Journal of Clinical Epidemiology, Vol 64, Gordon Guyatt, Andrew D. Oxman, Elie A. Akl, Regina Kunz, Gunn Vist, et al. GRADE guidelines: 1. Introduction GRADE evidence profiles and summary of findings tables. Pp. 383-394, Copyright 2011, with permission from Elsevier.

9-1 Appendix 9–1

Question Example DRUG INFORMATICS CENTER St. Anywhere Hospital Name of Inquirer: Address

Dr. Meghan J. Malone 2184 Fall St. Seneca Falls, NY 13148

Telephone Number:

(315)555-1212

Date: XX/XX/XX Time Received: XX:XX am/pm Time Required: 5 hours Nature of Request: Therapeutics Type of Inquirer: Pharmacist

QUESTION A young, adult male patient recently arrived from Japan and presented to the physician sparse medical records indicating he is suffering from tsutsugamushi disease. Because of the language difficulties, little is known about the patient, other than he is taking drug X for the illness. Physical examination reveals a patient in some discomfort with elevated temperature, swollen lymph glands, and red rash. All other findings appear to be normal. (Note: The person answering this question obtained as much background as possible about the patient.) The physician has little information on the disease and would like to know if that drug X is the most appropriate treatment.

ANSWER Tsutsugamushi disease is an acute infectious disease seen in harvesters of hemp in Japan.1 It is caused by Rickettsia tsutsugamushi. Common symptoms of the disease include fever, painful swelling of the lymph glands, a small black scab in the genital region, neck or axilla, and large dark-red papules. The disease is known by a number of other names, including akamushi disease, flood fever, inundation fever, island disease, Japanese river fever, and scrub typhus.2-4 (Note: Background information presented.) The standard treatment of the disease includes either drug X or drug Y, although there are several other less effective treatments.5-7 In the remainder of this paper, a comparison of the two major drugs will be presented. (Note: Clear objective for paper is presented.) A thorough search of the available literature was conducted. Unfortunately, there were few textbooks available on this disease. A search of MEDLINE® (1966 to present) and EMBASE’s Drugs and Pharmacology (1980 to present) produced a number of articles that were obtained and are reviewed below. (Note: This documents the type of search and acts as a lead-in to the remainder of the body of the paper.)

1100

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1101

Smith and Jones8 performed a double-blind, randomized comparison of the effects of drug X and drug Y in patients with tsutsugamushi fever. Patients were required to be between 18 and 70 years old, and could not have any concurrent infection or disorder that would affect the immune response to the disease (e.g., neutropenia, AIDS). Twenty patients received 10 mg of drug X three times a day for 15 days. Eighteen patients received 250 mg of drug Y twice a day for 10 days. The two groups were comparable, except that the patients receiving drug X were an average of 5 years younger (p < 0.05). Drug X was shown to produce a cure, both in terms of symptoms and cultures in 85% of patients, whereas drug Y only produced a cure in 55.5% of patients. The difference was statistically significant (p < 0.01). No significant adverse effects were seen in either group. Although it appears that drug X was the better agent, it should be noted that drug Y was given at its minimally effective dose, and may have performed better in a somewhat higher dose or longer regimen. (Note: Evaluative comments made about article.) (Note: Other articles would be described at this point.) Based on the literature found, it appears that drug Y is generally accepted as the better agent, except in those patients with severe renal insufficiency. Because this patient does not appear to be suffering from that problem, it is recommended that he receive a 3-week course of drug Y at a dose of 500 mg three times a day. Renal function should be monitored weekly. The patient should receive an additional week of therapy, if the symptoms have not been gone for the final week of therapy. (Note: This patient’s situation was specifically addressed, rather than just presenting a general conclusion.) Signature:

Date: March 20, 2014 Mia Q. Pharmacist, Pharm.D.

REFERENCES (Present references here.)

9-2 Appendix 9–2

Abstracts Abstracts are a synopsis (usually 250 words or less) of the most important aspect of an article. They should be clear, concise, and complete enough for readers to have a reasonable understanding of the important portions of the article.1 Since they are the most commonly read part of an article, they must be accurate and avoid the three most common errors: differences in information presented in the abstract and in the body of the article, information given in the abstract that was not presented in the article, and conclusions presented in the abstract that are not supported by information in the abstract.2-4 There are basically three types of abstracts that are seen in the literature. The first two (descriptive and informational) are somewhat traditional; however, they do not convey as much information as structured abstracts. Structured abstracts were originally designed to convey more information, and have been in use since the 1980s. The type of abstract to be used depends on the type of information and the requirements of the particular place the work is being submitted or used. In addition to writing an abstract, some journals ask that indexing terms be submitted. Whenever possible, Medical Subject Headings (MeSH) from the National Library of Medicine should be used for the indexing terms. Each of the abstracts will be discussed in more detail in the following sections.

DESCRIPTIVE ABSTRACTS A descriptive abstract, as its name implies, simply describes the information found in an article. Few specific details are given and it is primarily used in a review article. An example of this type of abstract is as follows: Lists of references that should be available, depending on location of the drug information service, are presented. These lists are specific to community, hospital, long-term care facility, and academic sites. Included are general references, indexing and abstracting services, and journals. Specialty references that would be useful in specific circumstances are also presented. In addition, the equipment and software necessary to access the computerized resources is shown for the individual references.

INFORMATIONAL ABSTRACTS Informational abstracts concisely summarize the factual information presented in a study. This type of abstract is more applicable to clinical studies.

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Key points to include in an informational abstract: • Study design (e.g., double-blind, crossover) • Purpose • Number of patients and other demographic aspects • Dosages • Results • Conclusions

An example of this type of abstract is as follows: A double-blind, randomized comparison of the effects of drug X and drug Y was performed in patients with tsutsugamushi fever, in order to determine whether either drug was superior in efficacy or safety. Twenty patients received 10 mg of drug X three times a day for 15 days. Eighteen patients received 250 mg of drug Y twice a day for 10 days. The two groups were comparable, except that the patients receiving drug X were an average of 5 years younger (p < 0.05). Drug X was shown to produce a cure, both in terms of symptoms and cultures in 85% of patients, whereas drug Y only produced a cure in 55.5% of patients. The difference was statistically significant (p < 0.01). No significant adverse effects were seen in either group. Drug X was shown to be significantly better than drug Y in the treatment of tsutsugamushi fever.

STRUCTURED ABSTRACTS Due to perceived deficiencies in abstracts,5 including lack of sufficient information,6 a new type of abstract was presented in 19877 and later updated in 1990.8 This structured abstract was designed to present more information about clinical studies and possibly laboratory studies, as compared to the informational abstract presented earlier.9,10 This type of abstract is not meant for case reports, studies of tissues or animals, opinion articles, and position papers.7 Abstracts following this standard seem to be gaining in popularity11 and have been mandated by an influential group of journals (e.g., The New England Journal of Medicine,12 Annals of Internal Medicine,7 JAMA: Journal of the American Medical Association,8 British Medical Journal,13 Canadian Medical Association Journal,14 Chest15), sometimes in a somewhat modified form, although this type of abstract may not be used in the majority of articles found in popular medical journals.16 This type of abstract has also been suggested in the pharmacy literature.17 Although the overall acceptance and approval of this format of abstract appears to be good, there are some who disapprove.18-20 Also, there is some data suggesting that structured abstracts do not always contain as much information as they should, if the published rules are followed,21 and that they do not necessarily contain any more useful information than informational abstracts.22 It is worth noting that articles with structured abstracts are indexed with a greater number of terms in MEDLINE®, which may make it easier to find such articles in a computer search.23 An abstract following this procedure would contain the following subheadings and information: • Objective—The main objective of the study and key secondary objectives. • Design—The basic design of the study (e.g., randomized, double-blind, crossover, placebo-

controlled, prospective versus retrospective) and duration of any follow-up.

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• Setting—The location and level of clinical care available at that location (e.g., tertiary care •

• •

•

•

hospital, ambulatory clinic). Also, if a single or multiple locations were involved. Patients or other participants—Description of the patients, including illnesses and key sociodemographic features, and how they were selected for the study (including whether it was a random, volunteer, etc. sample); it should also include the number of patients who refused to enroll in the study, proportion of the patients completing the study, and number of patients withdrawn due to adverse effects or other reasons. Intervention(s)—A brief description of any treatment(s) or intervention(s). Main outcome measure(s)—The main study outcome measurements, as planned before data collection was begun; if most of the article covers other material (e.g., data or hypotheses not planned to be observed before the study was started), that should be made clear. Results—The method(s) by which patients were assessed and the main results of the study, including any blinding. Statistical significance (particularly confidence intervals, odds ratios, numerators, and denominators) and levels of significance should be mentioned. Absolute, rather than relative, differences are presented (e.g., “adverse effects were seen in 5% of patients in group A and 10% of patients in group B,” rather than “group B had twice as many adverse effects”). The response rate should be provided in survey articles. Conclusion(s)—The key conclusion(s) directly supported by the evidence presented in the study and their clinical application(s) as well as a statement regarding whether further study is necessary should be included.

An example of this type of abstract is as follows: Study objective— To compare the safety and efficacy of drug X and drug Y in the treatment of tsutsugamushi fever. Design—Randomized, double-blind trial Setting—Tertiary care, military hospital located on Guam Patients—Sequential sample of 40 young (age 20 to 37), otherwise healthy male patients with tsutsugamushi fever. Patients randomly divided into two equal groups. Two patients were removed from the group receiving drug Y, due to transfer to U.S. mainland hospitals. The two groups were comparable, except that the patients receiving drug X were an average of 5 years younger (p < 0.05). Interventions—Twenty patients received 10 mg of drug X three times a day for 15 days. Eighteen patients received 250 mg of drug Y twice a day for 10 days. Main outcome measures—Physician and patients’ global assessment of disease activity; fivepoint scale from 0 (no symptoms) to 5 (severe disability). Presence or absence of organism on laboratory specimens. Results—Drug X was shown to produce a cure, both in terms of symptoms and cultures in 85% of patients, whereas drug Y only produced a cure in 55.5% of patients. The difference was statistically significant (p < 0.01). No significant adverse effects were seen in either group. Conclusions—Drug X was shown to produce significantly higher cure rates than drug Y in the treatment of tsutsugamushi fever, with no difference in adverse effects. Additional trials at different doses and lengths of therapy should be performed.

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A method to prepare a structured abstract for a review article differs from the first example.24 This method would only be applicable in specific situations, where a number of similar studies were evaluated together. It would not be useful in a situation where a number of dissimilar articles dealing with the same topic were discussed (e.g., a review of all therapies for a particular disease). Such an abstract would consist of the following items: • Purpose—The main objective of the review article, including information about the population

tested, how they were tested, and the outcome. • Data sources—A brief summary of data sources and the time periods covered. • Study selection—The number of studies covered in the article and how they were selected for

inclusion. • Data extraction—A description of the guidelines for abstracting data and how those guidelines were applied. • Data synthesis—The main results of the review and the method to obtain the results are outlined. • Conclusions—Important conclusions, including applications and need for further study. An example of this type of abstract would be as follows: Purpose—To evaluate the effect of the antihistamine, drug X, on symptoms of allergic reactions, as determined by physicians’ and patients’ global symptom assessment. Data sources—Studies published from January 1980 to December 2013 were identified by computer searches of MEDLINE® and Embase—Drugs and Pharmacology—and hand searching of bibliographies of the articles identified via the computer search. Study selection—Fifty-three studies evaluating the effects of drug X in the treatment of allergies were located. Data extraction—Descriptive data regarding the population, dosing, effects, and adverse effects were assessed, along with the study’s quality. Results of data analysis—Subjective and objective measures of effectiveness demonstrated that drug X decreased or eliminated allergy symptoms approximately 80% of the time in a variety of patient types (e.g., seasonal allergic rhinitis, perennial allergic rhinitis, anaphylaxis). The only adverse effects seen were dryness of mucous membranes and sedation, seen in approximately 5% and 2% of patients, respectively. Conclusions—Drug X is an effective agent for the treatment of allergic reactions. It has a low incidence of typical antihistamine adverse effects. Further studies should be performed to verify the effectiveness of Drug X in comparison to other drugs commonly used for anaphylaxis. A version of a structure abstract has also been proposed for use in describing clinical practice guidelines (see Chapter 7 for more information about such guidelines).25 The format is as follows: • Objective—Provides the primary objective of the guideline. This must include the health problem, along with the targeted patients, providers, and settings. • Options—This includes the various clinical practice options that were considered when the guideline was formulated. • Outcomes—Presents the significant health and economic outcomes that were considered when the alternative practices were considered.

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• Evidence—Describes how and when the evidence was gathered, selected for use, and synthe• • • • •

sized. Values—Describes how values were assigned to the potential outcomes, along with who was involved in doing so. Benefits, harms, and costs—Provides both the type and magnitude of the benefits, harms, and costs that might be expected from using the guideline. Recommendations—Provides a summary of the key recommendations. Validation—Describes any external validation of the guidelines that was conducted. Sponsors—Provides a list of the people who developed, funded, and/or endorsed the guideline.

An example of this type of abstract would be as follows: • Objective—To determine the best initial therapy for allergic rhinitis. This guideline is intended

•

• •

•

•

•

• •

for physicians and pharmacists to determine the best way to start therapy, particularly in the community. Options—Different nonprescription medications are considered first (antihistamines, decongestants [systemic and local], cromolyn), with the place for prescription intranasal steroids in initial therapy. Outcomes—The major outcomes evaluated are relief of symptoms, adverse effects, and direct cost to the patient. Evidence—All randomized, controlled clinical trials published in English found in MEDLINE from 1990 to 2012 were considered. In regard to costs, the average retail cost of the medication and the cost of a physician visit (for prescription medications) were calculated, based on the therapy used in the studies. Values—A group of three board certified allergy physicians and three pharmacists (Pharm.D. and either Board Certified Pharmacotherapy Specialist or Board Certified Ambulatory Care Pharmacist) were appointed by the American Academy of Allergy, Asthma & Immunology to review and evaluate the studies and pharmacoeconomic data. Patients were not represented. Benefits, harms, and costs—Use of a second-generation antihistamine (e.g., loratadine, cetirizine) provides the greatest efficacy with the least cost. Decongestants may be of value in the first few days, but are contraindicated in hypertensive patients. All other therapies have less effectiveness or greater cost. Recommendations—Patients suffering from allergies should begin therapy with a secondgeneration antihistamine, preferably about a week prior to anticipated allergen exposure. If therapy cannot be started early, pseudoephedrine may be used for up to 3 days as part of initial therapy in patients without hypertension or other contraindication. If this therapy is not sufficient, patients should be evaluated by a physician and prescribed an intranasal steroid spray. Validations—This recommendation was reviewed by three reviewers in the normal peerreview process established by the American Academy of Allergy, Asthma & Immunology. Sponsors—Development of this recommendation was funded by the American Academy of Allergy, Asthma & Immunology.

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REFERENCES 1. Staub NC. On writing abstracts. Physiologist. 1991;34:276-7. 2. Pitkin RM, Branagan MA. Can the accuracy of abstracts be improved by providing specific instructions? A randomized controlled trial. JAMA. 1998;280:267-9. 3. Pitkin RM, Branagan MA, Burmeister LF. Accuracy of data in abstracts of published research articles. JAMA. 1999;281:1110-1. 4. Winker MA. The need for concrete improvement in abstract quality. JAMA. 1999;281:1129-30. 5. Huth EJ. Structured abstracts for papers reporting clinical trials. Ann Intern Med. 1987;106:626-7. 6. Narine L, Yee DS, Einarson TR, Ilersich AL. Quality of abstracts of original research articles in CMAJ in 1989. CMAJ. 1991;144:449-53. 7. Ad Hoc Working Group for Critical Appraisal of the Medical Literature. A proposal for more informative abstracts of clinical articles. Ann Intern Med. 1987;106:598-604. 8. Haynes RB, Mulrow CD, Huth EJ, Altman DG, Gardner MJ. More informative abstracts revisited. Ann Intern Med. 1990;113:69-76. 9. Rennie D, Glass RM. Structuring abstracts to make them more informative. JAMA. 1991;266:116-17. 10. Haynes RB. Dissent. More informative abstracts: current status and evaluation. J Clin Epidemiol. 1993;46:595-7. 11. Ripple AM, Mork JG, Knecht LS, Humphreys BL. A retrospective cohort study of structured abstracts in MEDLINE, 1992-2006. J Med Libr Assoc. 2011;99(2):160-163. 12. Relman AS. New “Information for Authors”—and readers. NEJM. 1990;323:56. 13. Lock S. Structure abstracts. Now required for all papers reporting clinical trials. BMJ. 1988;297:156. 14. Squires BP. Structured abstracts of original research and review articles. CMAJ. 1990;143:619-22. 15. Soffer A. Abstracts of clinical investigations. A new and standardized format. Chest. 1987;92:389-90. 16. Nakayama T, Hirai N, Yamazaki S, Naito M. Adoption of structured abstracts by general medical journals and format for a structured abstract. J Med Libr Assoc. 2005;93(2):237242. 17. Kane-Gill S, Olsen KM. How to write an abstract suitable for publication. Hosp Pharm. 2004;39: 289-92. 18. Spitzer WO. Second thoughts. The structured sonnet. J Clin Epidemiol. 1991;44:729. 19. Heller MB. Dissent. Structured abstracts: a modest dissent. J Clin Epidemiol. 1991;44:739-40. 20. Heller MB. Structured abstracts. [letter] Ann Intern Med. 1990;113:722. 21. Froom P, Froom J. Variance and dissent: Presentation. Deficiencies in structured medical abstracts. J Clin Epidemiol. 1993;46:591-4. 22. Scherer RW, Crawley B. Reporting of randomized clinical trial descriptors and use of structured abstracts. JAMA. 1998;280:269-72. 23. Harbourt AM, Knecht LS, Humphreys BL. Structured abstracts in MEDLINE®, 1989–1991. Bull Med Libr Assoc. 1995:83(2):190-5. 24. Mulrow CD, Thacker SB, Pugh JA. A proposal for more informative abstracts of review articles. Ann Intern Med. 1988;108:613-5. 25. Hayward RSA, Wilson MC, Tunis SR, Bass EB, Rubin HR, Haynes RB. More informative abstracts of articles describing clinical practice guidelines. Ann Intern Med. 1993;118:731-7.

9-3 Appendix 9–3

Bibliography Although there seems to be a different method to prepare a bibliography for every English class ever given, there is fortunately a standardized method to prepare a bibliography in medical writing. This method is used by the National Library of Medicine and has been incorporated into the Uniform Requirements for Manuscripts Submitted to Biomedical Journals and published in Citing Medicine1-3; it has been used widely since the 1970s in both journals and other medical writing. This method will be presented here. References in the bibliography are placed in the order they are first cited in the text of a document, and each reference is assigned a consecutive Arabic number. Those cited only in tables or figures are numbered according to the place the table or figure is identified in the text. References are not listed multiple times in the bibliography, if they are cited more than once in the text of the document. Instead, subsequent citations to the same reference use the original reference number. It should also be noted that Ibid is not used. The reference number in the text will be the Arabic number in parenthesis or, commonly, superscript. This number is often cited after the sentence that contains the fact being referenced. If there are several references used to prepare a specific sentence, they may be listed at the end of the sentence or throughout the sentence. Also, if the sentence is a lead-in to an abstract, the authors’ names are commonly listed followed by the reference number. See the sentences below for examples. • Drug X has been shown to cause green rash with purple spots.2,3 • Drug Y is useful in the treatment of hypertension,4 congestive heart failure,5 and arrhythmias.6 • Smith and Jones7 studied the effects of … • Brown et al.9 treated … (please notice on this example, al. is followed by a period since it is

an abbreviation, whereas et is a full Latin word, and there is no need for a comma after the first author’s name) • Brown and associates9 treated … (this is used the same way as the previous example, but is preferred by some people over the use of et al.) Before getting into the method for listing references and examples, it should be mentioned that there are a number of general rules to be followed: • Citations are often not found in conclusions of documents. The conclusions are based on the

information presented, and cited, earlier in the article. • Avoid using abstracts as references, if at all possible. Sometimes the information is only published as an abstract, so it is necessary to cite the abstract in this situation.

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• Avoid using unpublished observations or personal communications as references. In the lat-

•

• • •

ter case, it is proper to insert references to written, but not oral, communications in parentheses in the text only and indicate it will not be formally referenced at the end of publication. Permission must be obtained from the author for the use of this material and this should only be used if the material is not available from a public source of information. A note should be made at the end of the publication stating that permission was given. If reference is made to an article that has been accepted by a journal, but not yet published, the phrase Forthcoming followed by the year of planned publication should be inserted where the volume and page numbers would normally be listed. It is usually necessary to get permission to cite this type of article, since the publisher may have strict confidentiality rules, and verification of acceptance by the journal should be obtained if that is the case. The use of the Internet has increased substantially, so it is important to use the correct citation based on the medium used. Only place a period at the end of a Web address if a back slash is the last character in the address. For items such as wikis, blogs, databases, when they are still open (people can still add something), use a hyphen with three spaces following it for date of publication. If they are closed (people can no longer add something), list the range of dates it was open.

Examples and some general templates of the above-mentioned and other types of references used in a bibliography are provided in the balance of this appendix. Please note that these should provide adequate direction in how to cite most publications. However, if detailed directions and further examples are needed, the reader is referred to Citing Medicine, which is available free on the Internet at http://nlm.nih.gov/citingmedicine.3

JOURNAL ARTICLES To cite a journal article, the following information should be given: • Last name of author(s) and initials each separated by commas, with a period at the end. Please

note that some publications will list only three or six authors followed by the phrase et al. • Title of article (do not use quotation marks, capitalize only the initial word of title and proper

• • • •

nouns in English) followed by a period with the exception when punctuation is already at the end of the title—for example, if a title ends with a question mark or exclamation point, use that instead of the period. Journal Title (abbreviated as found in the list of journals at http://www.nlm.nih.gov/tsd/ serials/lji.html) followed by a period. Date of Publication (year, month [three-letter abbreviation], and day of month [if available]) followed by a semicolon. Volume number (listing the issue number in parenthesis) followed by a colon. Page numbers (If continuous, use first and last pages separated by a hyphen. Keep page numbers concise (e.g., 561-569 should be 561-9). If separate pages, list the pages separated by a comma and a space. If a combination of continuous and separate pages, use both (e.g., 18-29, 33, 40) followed by a period. If an Internet article does not have page numbers, put the

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actual number of pages in square brackets, followed by p. (e.g., [20 p.]). If the article is in unpaginated format (e.g., html, xml), precede the number with the word about (e.g., [about 10 screens] or [about 15 p.]) A condensed version of the above information is as follows: Author(s). Title of article. Journal Title. Date of Publication;Volume Number(Issue Number): Page Number(s).

Journal on the Internet Author(s). Title of article. Journal Title [Internet]. Date of Publication [Date of Update; Date of Citation];Volume Number(Issue Number):Page Number(s) or [Length of Article]. Available from: Web Address

Example Journal Citations Standard Journal Article Smythe M, Hoffman J, Kizy K, Dmuchowski C. Estimating creatinine clearance in elderly patients with low serum creatinine concentrations. Am J Hosp Pharm. 1994 Jan 15;51:198-204. Beck DE, Aceves-Blumenthal C, Carson R, Culley J, Noguchi J, Dawson K, Hotchkiss G. Factors contributing to volunteer practitioner-faculty vitality. Am J Pharm Ed. 1993 Apr;57:305-12.

Journal Article on the Internet Robinson ET. The pharmacists as educator: implications for practice and education. Am J Pharm Ed [Internet]. 2004 [cited 2010 Jun 17];68(3):[4 p.]. Available from: http://www.ajpe.org/aj6803/ aj680372/aj680372.pdf Nemecz G. Evening primrose. US Pharmacist [Internet]. 1998 Nov [cited 1998 Dec 10];23: [about 1 p.]. Available from: http://www.uspharmacist.com/NewLook/Docs/1998/Nov1998/ EveningPrimrose.htm

Organization as Author Task Force on Specialty Recognition of Oncology Pharmacy Practice. Executive summary of petition requesting specialty recognition of oncology pharmacy practice. Am J Hosp Pharm. 1994 Jan 15;51:219-24.

Personal Authors and Organization as Author Wiencke K, Louka AS, Spurkland A, Vatn M, The IBSEN Study Group, Schrumpf E. Association of matrix metalloproteinase-1 and -3 promoter polymorphisms with clinical subsets of Norwegian primary sclerosing cholangitis patients. J Hepatol. 2004 Aug;41(2):209-14.

No Author Given N.Y. court rules against Medicaid co-pay. Drug Topics. 1994 Mar;138(3):6.

Article Not in English (Note That the Language Is Stated at the End) Antoni N. Zur kritjk der irrtümlich sogenannten sehnen- und periostreflexe. Acta Psychiatrica Neurologica. 1932;VII:9-19. German.

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Volume with Supplement Nayler WG. Pharmacological aspects of calcium antagonism. Short term and long term benefits. Drugs. 1993 Apr;46Suppl 2:40-7.

Issue with Supplement Graves NM. Pharmacokinetics and interactions of antiepileptic drugs. Am J Hosp Pharm. 1993 Dec;50(12 Suppl A):S23-9.

Volume with Part Katchen MS, Lyons TJ, Gillingham KK, Schlegel W. A case of left hypoglossal neurapraxia following G exposure in a centrifuge. Aviat Space Environ Med. 1990 Sep;61(Pt 2):837-9.

Issue with Part Dudley MN. Maximizing patient outcomes of antiinfective therapy. Pharmacotherapy. 1993 MarApr;13(2 Pt 2):29S-33S.

Issue with No Volume Slaga TJ, Gimenez-Conti IB. An animal model for oral cancer. Monogr J Nat Cancer Instit. 1992;(13): 55-60.

No Issue or Volume Payne R. Acute exacerbation of chronic cancer pain: basic assessment and treatments of breakthrough pain. Acute Pain Sympt Manage. 1998:4-5.

Pagination in Roman Numerals Koretz RL. Clinical nutrition. Gastroenterol Clin North Am. 1998 Jun;27(2):xi-xiii.

Expressing Type of Article (As Needed) Goldwater SH, Chatelain F. Taking time to communicate [letter]. Am J Hosp Pharm. 1994 Feb 1;51: 232, 234. Talley CR. Reducing demand through preventive care [editorial]. Am J Hosp Pharm. 1994 Jan 1;51:55. Saritas A, Cakir Z, Emet M, Uzkeser M, Akoz A, Acemoglu F. Factors affecting the b-type natriuretic peptide levels in stroke patients [abstract]. Ann Acad Med Singapore. 2010 May;39(5):385.

Article Containing a Retraction Brown MD. Retraction. Am Heart J. 1986;111:623. Retraction of: Slutsky RA, Olson LK. Am Heart J. 1984;108:543-7.

Article Retracted Slutsky RA, Olson LK. Intravascular and extravascular pulmonary fluid volumes during chronic experimental left ventricular dysfunction Am Heart J. 1984 Sep;108:543-7. Retraction in: Am Heart J. 1986 Mar;111:623.

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Article with Published Erratum Reitz MS Jr, Juo HG, Oleske J, Hoxie J, Popovic M, Read-Connole E. On the historical origins of HIV-1 (MN) and (RF) [letter]. AIDS Res Hum Retroviruses. 1992 Aug;8:1539-41. Erratum in: AIDS Res Hum Retroviruses 1992 Aug;8:1731.

Item (e.g., Table or Figure) in Article Hohnloser SH, Pajitnev D, Pogue J, Healey JS, Pfeffer MA, Yusuf S, Connolly SJ. Incidence of stroke in paroxysmal versus sustained atrial fibrillation in patients taking oral anticoagulation or combined antiplatelet therapy: an ACTIVE W substudy. J Am Coll Cardiol. 2007 Nov 22;50(22):2156-61. Table 4, Incidence of stoke or non-CNS systemic embolism in patients with paroxysmal versus persistent/permanent AF treated with aspirin plus clopidogrel or OAC; p. 2159.

Unpublished Article Malone PM. Topics in informatics. Adv Pharm. Forthcoming 2004.

BOOKS To cite a book, which can include manuals, brochures, or fact sheets, the following information should be given: • Last name of author(s) and initials each separated by a comma and followed by a period.

Some publications will list only three authors followed by the phrase et al. • Title of book (capitalize only the initial word of title and proper nouns in English) followed by

• •

• •

a period with the exception that punctuation is already at the end of the title—for example, if a title ends with a question mark or exclamation point, use that instead of the period Edition, other than first followed by period Place of publication (city) followed by colon (if the location is not clear with just a city name, the state or country abbreviation may be placed in parenthesis after the city name and before the colon) Name of publisher followed by semicolon Year of publication followed by period

A condensed version of the above information is as follows: Author(s). Title of Book. Edition. Place of Publication: Publisher; Date of Publication.

Book on the Internet: Author(s). Title of Book [Internet]. Place of Publication: Publisher; Date of Publication [Date of Update; Date of Citation]. Available from: Web Address

Example Book Citations Standard Book Albright RG. A basic guide to online information systems for health care professionals. Arlington (VA): Information Resource Press; 1988.

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Book on the Internet DiPiro JT, Talbert RL, Yee GC, Matzke GR, Wells BG, Posey LM, editors. Pharmacotherapy: a pathophysiologic approach [Internet]. 7th ed. New York: McGraw-Hill; 2008 [cited 2010 Jun 15]. Available from: http://www.accesspharmacy.com/resourceToc.aspx?resourceID = 406 Lacy CF, Armstrong LL, Goldman MP, et al, editors. Lexi-comp online [Internet]. Hudson (OH): LexiComp, Inc.; c1978-2010 [cited 2010 Jun 16]. Available from: http://online.lexi.com/crlsql/ servlet/crlonline

Editor(s) as Author Chisholm-Burns MA, Wells BG, Schwinghammer TL, Malone PM, Kolesar JM, Dipiro JT, editors. Pharmacotherapy principles and practice. 3rd ed. New York: McGraw-Hill; 2013.

No Specific Editor(s), Compiler, or Author Identified Drug facts and comparisons 1999. St. Louis: Facts and Comparisons; 1998.

Organization as Author and Publisher United States Pharmacopeial Convention, Inc. USAN and the USP dictionary of drug names. Rockville: United States Pharmacopeial Convention, Inc.; 1993.

Volumes (Same Author(s)/Editor(s)) United States Pharmacopeial Convention, Inc. USP dispensing information. 22nd ed. Vol. 2, Advice for the patient: drug information in lay language. Greenwood Village (CO): Micromedex; 2002. If on the Internet use the following format: Author(s). Title of Book. Volume Number, Volume Title [Internet]. Place of Publication: Publisher; Date of Publication [Date of Update; Date of Citation]. Available from: Web Address Ross IA. Medicinal plants of the world. Vol. 3, Chemical constituents, traditional and modern medicinal uses [Internet]. Totowa (NJ): Humana Press, Inc.; 2005 [cited 2010 Jun 23]. Available from: http://metis.findlay.edu:2080/xtf-ebc/search?keyword = pharmacy

Portion of a Book (e.g., Chapter, Table, Figure, or Appendix) with Author(s) Writing Entire Book Bauer LA. Applied clinical pharmacokinetics. 2nd ed. New York: McGraw-Hill; 2008. Chapter 6, Digoxin; p. 301-55. If on the Internet, use the following format: Author(s). Title of Book [Internet]. Place of Publication: Publisher; Date of Publication [Date of Update of Book]. Portion Number, Portion Title; [Date of Update of Portion; Date of Citation]; Page Number(s) or [Length of Portion]. Available from: Web Address Bauer LA. Applied clinical pharmacokinetics [Internet]. 2nd ed. New York: McGraw-Hill; 2008. Chapter 6, Digoxin; [cited 2010 Jun 23]; [about 20 screens]. Available from: http://metis.findlay. edu:2209/content.aspx?aID = 3519569

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Contribution to Book (Portions of Book Written by Different Authors) Malesker MA, Morrow LE. Fluids and electrolytes. In: Chisholm-Burns MA, Schwinghammer TL, Wells BG, Malone PM, Kolesar JM, Dipiro JT, editors. Pharmacotherapy principles and practice. 2nd ed. New York: McGraw-Hill; 2010. p. 479-94. If on the Internet, use the following format: Chapter Author(s). Chapter Title. In: Author(s)/Editor(s). Title of Book [Internet]. Place of Publication: Publisher; Date of Publication [Date of Citation]. Available from: Web address Malone PM. Professional writing. In: Malone PM, Kier KL, Stanovich JE, editors. Drug information: a guide for pharmacists [Internet]. 4th ed. New York: McGraw-Hill; 2012 [cited 2012 Oct 17]. Available from: http://www.accesspharmacy.com/content.aspx?aid = 55673619

Book on CD-ROM or DVD Haux R, Kulikowski C. Yearbook 04 of medical informatics – towards clinical bioinformatics [CD-ROM]. Stuttgart (Germany): Schatteuer; 2004.

Video Clip, Videocast, or Podcast Associated with Book Author(s). Title of Book [Internet]. Place of Publication: Publisher; Date of Publication. [Video or Videocast or Podcast], Title of Video; [Date of Citation]; [Length of Video]. Available from: Web Address Brunton LL, Parker KL, Murri N, Blumenthal DK, Knollmann BC, editors. Goodman and Gilman’s: the pharmacological basis of therapeutics [Internet]. 11th ed. New York: McGraw-Hill; 2006 [Video], Adrenergic neuroeffector junction; [cited 2010 Jun 23]; [5 min.]. Available from: http:// metis.findlay.edu:2209/video.aspx?file = anj_01/anj_01

OTHER MATERIAL Format and Example Citations (in alphabetical order) Conference Proceedings Editor(s). Conference Title; Date(s) of Conference; Conference Location. Place of Publication: Publisher; Date of Publication. Allebeck P, Jansson B, editors. Ethics in medicine. Individual integrity versus demands of society. Karolinska Institute Novel Conference Series. Proceedings of the 3rd International Congress on Ethics in Medicine; 1989 Sep 13-15; Stockholm. New York: Raven Press; 1990. If on the Internet, use the following format: Editor(s). Conference Title [Internet]; Date(s) of Conference; Conference Location. Place of Publication: Publisher; [Date of Citation]. [Length of Publication]. Available from: Web Address Allebeck P, Jansson B, editors. Ethics in medicine. Individual integrity versus demands of society. Karolinska Institute Novel Conference Series. Proceedings of the 3rd International Congress on Ethics in Medicine [Internet]; 1989 Sep 13-15; Stockholm. New York: Raven Press; [cited 2010 Jun 23]. [8 p.] Available from: http://jmp.oxfordjournals.org/cgi/issue_pdf/backmatter_pdf/13/4.pdf

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Conference Paper Author(s) of Conference Paper. Title of Paper. In: Editors of Conference Proceedings. Conference Title; Date(s) of Conference; Conference Location. Place of Publication: Publisher; Date of Publication. Pages. Keyserlingk E. Ethical guidelines and codes—can they be universally applicable in a multi-cultural world? In: Allebeck P, Jansson B, editors. Ethics in medicine. Individual integrity versus demands of society. Karolinska Institute Novel Conference Series. Proceedings of the 3rd International Congress on Ethics in Medicine; 1989 Sep 13-15; Stockholm. New York: Raven Press; 1990. p. 137-49. If on the Internet, use the following format: Author(s) of Conference Paper. Title of Paper. In: Editors of Conference Proceedings. Conference Title [Internet]; Date(s) of Conference; Conference Location. Place of Publication: Publisher; Date of Publication [Date of Citation]. [Length of Paper]. Available from: Web Address Keyserlingk E. Ethical guidelines and codes—can they be universally applicable in a multi-cultural world? In: Allebeck P, Jansson B, editors. Ethics in medicine. Individual integrity versus demands of society. Karolinska Institute Novel Conference Series. Proceedings of the 3rd International Congress on Ethics in Medicine [Internet]; 1989 Sep 13-15; Stockholm. New York: Raven Press; 1990 [cited 2010 Jun 23]. [about 2 p.]. Available from: http://jmp.oxfordjournals. org/cgi/issue_pdf/backmatter_pdf/13/4.pdf

Dictionary Definition Dictionary Name. Place of Publication: Publisher; Date of Publication.Word Being Defined; Page Number. Stedman’s medical dictionary. 27th ed. New York: Lippincott Williams & Wilkins; 2000. Asthenia; p. 158. If on the Internet, use the following format: Dictionary Name [Internet]. Place of Publication: Publisher; Date of Publication. Term Being Defined; [Date of Citation]. Available from: Web Address Merriam-Webster Online [Internet]. Springfield (MA): Merriam-Webster, Inc.; c2010. Blood pressure; [cited 2010 Jun 23]. Available from: http://www.merriam-webster.com/dictionary/blood%20 pressure

Dissertation/Thesis Author(s). Title [dissertation or master’s thesis]. Place of Publication: Publisher; Date of Publication. Wellman CO. Pain perceptions and coping strategies of school-age children and their parents: a descriptive-correlational study [dissertation]. Omaha (NE): Creighton University; 1985. If on the Internet, use the following format: Author(s). Title [dissertation or master’s thesis on the Internet]. Place of Publication: Publisher; Date of Publication [Date of Update; Date of Citation]. Available from: Web Address

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Mil JW. Pharmaceutical care, the future of pharmacy: theory, research, and practice [dissertation on the Internet]. Groningen (The Netherlands): University of Groningen; 2000 Feb 1 [updated 2009 Sep 8; cited 2010 Jun 23]. Available from: http://dissertations.ub.rug.nl/faculties/ science/2000/j.w.f.van.mil/?pLanguage = en&pFullItemRecord = ON

Legal Documents Please consult: The bluebook: a uniform system of citation. 19th ed. Cambridge (MA): Harvard Law Review Association; 2010.

Newspaper Article Author(s). Title of Article. Newspaper Title (Edition). Date of Publication;Section:Page Number(Column Number). Fein EB. Rise in fetal tests prompts ethical debate. The New York Times (National Ed.). 1994 Feb 5;Sect. A:1(col. 2). If on the Internet, use the following format: Author(s). Title of Article. Newspaper Title [Internet]. Date of Publication [Date of Update; Date of Citation];Section:Page Number or [Length of Article]. Available from: Web Address Painter K. Your health: feet bear the strain of extra weight. USA Today [Internet]. 2010 Jun 20 [cited 2010 Jun 23];Health and Behavior:[about 2 screens]. Available from: http://www.usatoday.com/ news/health/painter/2010-06-21-yourhealth21_ST_N.htm

Package Insert Package inserts are commonly cited in professional writing; however, the Uniform Requirements do not address the format to use. The following is a common format that is similar to those presented in this appendix. Medication Name [package insert]. Place of Publication: Publisher; Date of Publication. Prilosec® (omeprazole) delayed-release capsules [package insert]. Wayne, PA: Astra Merck; 1998 Jun. If on the Internet, use the following format: Medication Name [package insert on the Internet]. Place of Publication: Publisher; Date of Publication [Date of Update; Date of Citation]. Available from: Web Address Omeprazole [package insert on the Internet]. Bethesda (MD): U.S. National Library of Medicine; 2009 Aug [updated 2009 Dec; cited 2010 Jun 23]. Available from: http://dailymed.nlm.nih.gov/ dailymed/drugInfo.cfm?id = 14749

Meeting Presentations of Paper and Poster Sessions Author(s). Title of Paper or Poster. Paper or Poster session presented at: Conference Title; Date(s) of Conference; Conference Location. Ciaccia V, Hinders C, Malone M, Morales R, Sanchez A. Comparison of evidence based hypertension guideline model to an alternative model. Poster session presented at: The University of Findlay Symposium for Scholarship and Creativity; 2010 Apr 13; Findlay, OH.

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Patent Inventor(s); Assignee (Applicant). Title. Patent Country patent Country Code Patent Number. Date patent issued. Schwartz B, inventor; New England Medical Center Hospital, Inc., assignee. Method of and solution for treating glaucoma. United States patent US 5,212,168. 1993 May 18.

Personal Communication In text citation only (e.g. Letter from or Conversation with; unreferenced, see Notes Section) In Notes Section state that permission was given to reference the letter or conversation. (Letter from Max Jones to Charlie Smith on June 23, 2010; unreferenced, see Notes Section)

Scientific or Technical Report Author(s). Title. Place of Publication: Publisher; Date of Publication. Report No.: . Issued by funding/sponsoring agency: Shekelle P, Morton S, Maglione M (Southern California Evidence-Based Practice Center/RAND, Santa Monica, CA). Ephedra and ephedrine for weight loss and athletic performance enhancement: clinical efficacy and side effects. Vol. 1, Evidence report and evidence tables. Rockville (MD): Agency for Healthcare Research and Quality; 2003 Mar. (Evidence report/technology assessment; no. 78). Report No.: AHRQPUB03E022. Contract No.: AHRQ-290-97-001. Issued by performing agency: Shekelle P, Morton S, Maglione M. Ephedra and ephedrine for weight loss and athletic performance enhancement: clinical efficacy and side effects. Vol. 1, Evidence report and evidence tables. Santa Monica: Southern California Evidence-Based Practice Center/RAND; 2003 Mar. (Evidence report/technology assessment; no. 78). Report No.: AHRQPUB03E022. Contract No.: AHRQ-290-97-001. Sponsored by the Agency for Healthcare Research and Quality. If on the Internet, use the following format: Author(s). Title [Internet]. Place of Publication: Publisher; Date of Publication [Date of Citation]. Report No.: Available from: Web Address Qureshi N, Wilson B, Santaguida P, Carroll J, Allanson J, Culebro CR, Brouwers M, Raina P. Collection and use of cancer family history in primary care [Internet]. Rockville (MD): Agency for Healthcare Research and Quality; 2007 Oct [cited 2010 Jun 23]. (Evidence reports/technology assessments no. 159) Report No.: AHRQPUB08E001. Contract No.: 290- 02-0020. Available from: http://www.ncbi.nlm.nih.gov/bookshelf/br.fcgi?book = erta159

OTHER ELECTRONIC MATERIAL IN ALPHABETICAL ORDER Format and Example Citations (in alphabetical order)

Part of a Blog (Only One Author) Since this is personal communication, as above, it usually is done as an in-text citation only (Posting on given date from author on given blog; unreferenced, see Notes section). In Notes section, state that permission was given to reference the blog post. Otherwise follow the format given below.

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Author of Blog. Title of Blog [blog on the Internet]. Place of Publication: Publisher. [Start Date of Blog]. Title of Part; Date of Publication [Date of Citation]; [Length of Part]. Available from: Web Address Daria. Living with Cancer [blog on the Internet]. Edmonton (AB): Daria. [2008 Aug]. Chemo went well; 2010 Jun 12 [cited 2010 Jun 16]; [about 1 screen]. Available from: http://darialivingwithcancer.blogspot.com/.

Part of a Blog (Multiple Authors) Since this is personal communication, as above, it usually is done as an in-text citation only (Posting on given date from author on given blog; unreferenced, see Notes section). In Notes section, state that permission was given to reference the blog post. Otherwise follow the format given below. Author of Comment. Title of Blog Comment. Date of Publication of Comment [Date of Citation]. In: Author of Blog. Name of Blog [blog on the Internet]. Place of Publication: Publisher. Date of Publication- . [Length of Comment]. Available from: Web Address Smith J. Dialysis. 2010 Jun 16 [cited 2010 Jun 16]. In: Kidney Coaching Foundation, Inc. KCF Blog and News [blog on the Internet]. Raleigh (NC): Kidney Coaching Foundation, Inc. c2005-2010- . [about 1 paragraph]. Available from: http://www.thekcf.org/bn/

Computer Program on CD-ROM or DVD Author(s). Title [Medium]. Version. Place of Publication: Publisher; Date of Publication. A.D.A.M. Animated dissection of anatomy for medicine [CD-ROM]. Version 2.2. for Windows. Marietta (GA): A.D.A.M. Software, Inc.; 1993.

Database on the Internet Title of Database [Internet]. Place of Publication: Publisher. Date of Publication [Date of Update; Date of Citation]. Available from: Web Address PubMed [Internet]. Bethesda (MD): National Library of Medicine. 2004 [cited 2004 Aug 18]. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi DRUGDEX [Internet]. Greenwood Village (CO): Thomson Reuters Inc. c1974-2010 [cited 2010 Jun 16]. Available from: http://www.micromedex.com/products/drugdex/

Part of a Database on the Internet (e.g., a single drug monograph out of a publication) Title of Database [Internet]. Place of Publication: Publisher. Date of Publication. Record Identifier, Title of Part; [Date of Update; Date of Citation]; [Length of Part]. Available from: Web Address MeSH Browser [Internet]. Bethesda (MD): National Library of Medicine. 2004. unique ID: D015201, Phenytoin; [cited 2004 Aug 18]; [about 670 p.]. Available from: http://www.nlm.nih.gov/mesh/ MBrowser.html DRUGDEX [Internet]. Greenwood Village (CO): Thomson Reuters Inc. c1974-2010. Amiodarone; [updated 2010 Apr 23]; [about 8 screens]. Available from: http://www.micromedex.com/ products/drugdex/

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Electronic Mail Since this is personal communication, as above, it usually is done as an in-text citation only (Email on given date from sender to recipient; unreferenced, see Notes section). In Notes section, state that permission was given to reference the email. Otherwise follow the format given below. Author. Title of Email [Internet]. Message to: Recipient(s). Date of Message [Date of Citation]. [Length of Email]. Malone, Patrick. Drug information textbook [Internet]. Message to: John Stanovich; Mark Malesker. 2010 Jun 14 [2010 Jun 16]. [3 paragraphs].

Encyclopedia Entry Name of Encyclopedia [Internet]. Place of Publication: Publisher; Date of Publication. Name of Entry; [Date of Update; Date of Citation]; [Length of Entry]. Available from: Web Address Encyclopedia Britannica Online [Internet]. Chicago: Britannica; 2010. Stroke; [cited 2010 Jun 23]; [about 5 screens]. Available from: http://www.britannica.com/EBchecked/topic/569347/stroke

LISTSERV Since this is personal communication, as above, it usually is done as an in-text citation only (Posting on given date from sender to given LISTSERV; unreferenced, see Notes section). In Notes section, state that permission was given from the sender to reference the email. Otherwise follow the format given below. Author. Title of Message. In: Name of LISTSERV [Internet]. Place of Publication: Publisher; Date of Message [Date of Citation]. [Length of Message]. Malone PM. CAMIPR – discussion forum for medication information specialists. In: CAMIPR [Internet]. Iowa City: Consortium for the Advancement of Medication Policy and Research; 2010 May 6 [cited 2010 June 18]. [about 1 p.].

Video Clip, Videocast, or Podcast Title of Homepage [Internet]. Place of Publication: Publisher; Date of Publication of Homepage. [Video or Videocast or Podcast], Title of Video; Date of Publication of Video (if different from Homepage) [Date of Update; Date of Citation]; [Length of Video]. Available from: Web Address American Society of Health-System Pharmacists [Internet]. Bethesda (MD): ASHP Advantage; c2010. [Podcast], Multidisciplinary approach to identifying patients at risk for VTE; 2010 May 4 [cited 2010 Jun 23]; [45 min.]. Available from: http://www.ashpadvantage.com/podcasts/

Web site Homepage Author(s). Title of Homepage [Internet]. Place of Publication: Publisher; Date of Publication [Date of Update; Date of Citation]. Available from: Web Address American Society of Health-System Pharmacists [Internet]. Bethesda (MD): American Society of Health-System Pharmacists; c1997-2004 [updated 2004 Aug 18; cited 2004 Aug 18]. Available from: http://www.ashp.org/

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Part of a Web site Title of Homepage [Internet]. Place of Publication: Publisher; Date of Publication of Homepage. Title of Part; Date of Publication of Part (if different from Homepage) [Date of Update; Date of Citation]; [Length of Part]. Available from: Web Address American Society of Health-System Pharmacists [Internet]. Bethesda (MD): American Society of Health-System Pharmacists; c1997-2004. Compounding Resource Center; [updated 2004 Aug 18; cited 2004 Aug 18]; [about 1 screen]. Available from: http://www.ashp.org/ compounding

Wiki Since this is personal communication, as above, it usually is done as an in-text citation only (Posting on given date from author on given wiki; unreferenced, see Notes section). In Notes section, state that permission was given from the author to reference the wiki. Otherwise follow the format given below. Author of Part. Title of Part of Wiki. Date of Posting [Date of Update; Date of Citation]. In: Title of Wiki [Internet]. Place of Publication: Publisher. Start Date of Wiki- . [Length of Part]. Available from: Web Address If no author for part: Title of Wiki [Internet]. Place of Publication: Publisher. Start Date of Wiki- . Title of Part of Wiki; [Date of Update; Date of Citation]; [Length of Part]. Available from: Web Address Wiki Public Health [Internet]. [place unknown]: WikiPH. [date unknown]- . Health care; [updated 2007 Mar 27; cited 2010 Jun 16]; [about 2 screens]. Available from: http://wikiph.org/index. php?title = Health_care

REFERENCES 1. International Committee of Medical Journal Editors. Uniform requirements for manuscripts submitted to biomedical journals [Internet]. Philadelphia (PA): International Committee of Medical Journal Editors; 2009 [cited 2012 May 15]. Available from: http://www.icmje.org 2. International Committee of Medical Journal Editors. Uniform requirements for manuscripts submitted to biomedical journals: sample references [Internet]. Philadelphia (PA): International Committee of Medical Journal Editors; 2003 [updated 2011 July 15; cited 2012 May 15]. Available from: http://www.nlm.nih.gov/bsd/uniform_requirements.html 3. Patrias K. Citing medicine: the NLM style guide for authors, editors, and publishers [Internet]. 2nd ed. Wendling DL, technical editor. Bethesda (MD): National Library of Medicine (US); 2007 [updated 2011 Sep 15; cited 2012 May 15] Available from: http://nlm.nih.gov/citingmedicine

11–1 Appendix 11–1

Code of Ethics for Pharmacists1 Pharmacists are health professionals who assist individuals in making the best use of medications. This Code, prepared and supported by pharmacists, is intended to state publicly the principles that form the fundamental basis of the roles and responsibilities of pharmacists. These principles, based on moral obligations and virtues, are established to guide pharmacists in relationships with patients, health professionals, and society.

I. A pharmacist respects the covenantal relationship between the patient and pharmacist. Considering the patient–pharmacist relationship as a covenant means that a pharmacist has moral obligations in response to the gift of trust received from society. In return for this gift, a pharmacist promises to help individuals achieve optimum benefit from their medications, to be committed to their welfare, and to maintain their trust.

II. A pharmacist promotes the good of every patient in a caring, compassionate, and confidential manner. A pharmacist places concern for the well-being of the patient at the center of professional practice. In doing so, a pharmacist considers needs stated by the patient as well as those defined by health science. A pharmacist is dedicated to protecting the dignity of the patient. With a caring attitude and a compassionate spirit, a pharmacist focuses on serving the patient in a private and confidential manner.

III. A pharmacist respects the autonomy and dignity of each patient. A pharmacist promotes the right of self-determination and recognizes individual self-worth by encouraging patients to participate in decisions about their health. A pharmacist communicates with patients in terms that are understandable. In all cases, a pharmacist respects personal and cultural differences among patients.

IV. A pharmacist acts with honesty and integrity in professional relationships. A pharmacist has a duty to tell the truth and to act with conviction of conscience. A pharmacist avoids discriminatory practices, behavior or work conditions that impair professional judgment, and actions that compromise dedication to the best interests of patients.

V. A pharmacist maintains professional competence. A pharmacist has a duty to maintain knowledge and abilities as new medications, devices, and technologies become available and as health information advances.

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VI. A pharmacist respects the values and abilities of colleagues and other health professionals. When appropriate, a pharmacist asks for the consultation of colleagues or other health professionals or refers the patient. A pharmacist acknowledges that colleagues and other health professionals may differ in the beliefs and values they apply to the care of the patient.

VII. A pharmacist serves individual, community, and societal needs. The primary obligation of a pharmacist is to individual patients. However, the obligations of a pharmacist may at times extend beyond the individual to the community and society. In these situations, the pharmacist recognizes the responsibilities that accompany these obligations and acts accordingly.

VIII. A pharmacist seeks justice in the distribution of health resources. When health resources are allocated, a pharmacist is fair and equitable, balancing the needs of patients and society. ∗ Adopted by the membership of the American Pharmacists Association October 27, 1994.

REFERENCE 1. American Pharmacists Association. Code of ethics 1994 [Internet]. [updated Oct 27, 1994 Mar 26, 2010]. Available from: http://www.pharmacist.com/code-ethics.

12–1 Appendix 12–1

Pharmacy and Therapeutics Committee Procedure This appendix includes two policy and procedure operational statements. The first is specifically written to centralize the formulary decision process for a multihospital health system: a Formulary Committee. The second, and closely related, operational statement is written as a model to function as the traditional Pharmacy and Therapeutics Committee for a Hospital’s Medical Staff. Both operational statements describe the functions of their related committees based on a certain degree of autonomy. Their membership is ultimately chosen by an administrative leader as a means to best isolate the committee from organizational as well as economic influences. The decision process for each operational statement is intended to create predictability and transparency. To implement this set of operational statements, each Executive Committee of the hospitals in a multihospital system would pass the following resolution: The Medical Staff of Alpha Hospital agrees to delegate its Pharmacy and Therapeutic Committee responsibilities to ALPHAOMEGA HEALTH based on the policy and procedures for a “Hospital Formulary System” and a “Hospital Pharmacy and Therapeutics Committee.” The two operational statements can also be combined to reflect the traditional functions of a single hospital, medical staff-based pharmacy, and therapeutics committee. Also, there may be other arrangements where the two operational statements could provide the organizational environment for a closed health system, a pharmacy benefits manager, or one of the new organizational structures created by Federal Legislation in 2004 for the new financing of drug coverage in the United States.

POLICY TITLE: HOSPITAL FORMULARY SYSTEM I. PURPOSE To maintain a HOSPITAL FORMULARY and a Formulary Committee for all ALPHAOMEGA HEALTH Hospitals as a means to enhance the quality of health care for all patients served by ALPHAOMEGA HEALTH

II. POLICY A. The Formulary Committee of ALPHAOMEGA HEALTH will periodically evaluate its performance as a means to improve its ability to support the Vision and Mission of ALPHAOMEGA HEALTH.

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B. ALPHAOMEGA HEALTH will maintain one Formulary Committee and a Pharmacy and Therapeutics Committee (P&T COMMITTEE) at each ALPHAOMEGA HEALTH Hospital to implement this POLICY in accord with the applicable Medical Staff Bylaws and this POLICY. C. The Formulary Committee of ALPHAOMEGA HEALTH will maintain a standard format for a HOSPITAL FORMULARY that is based on the provisions of this POLICY. D. The Formulary Committee will develop and continually revise a list of therapeutic products, a HOSPITAL FORMULARY, that reflects the current clinical judgment of the Medical Staff of ALPHAOMEGA HEALTH Hospitals regarding the selection of the best therapeutic products for the health care of hospitalized patients. The Formulary Committee will evaluate the various alternative therapeutic products available and develop the HOSPITAL FORMULARY based on an evaluation of each therapeutic product’s indications, effectiveness, risks, patient safety, and overall impact on health care costs. E. The Formulary Committee will collaborate with the P&T Committee at each ALPHAOMEGA HEALTH Hospital to monitor compliance with the provisions of the HOSPITAL FORMULARY. F. The Formulary Committee will support the quality improvement functions of ALPHAOMEGA HEALTH where necessary to improve the use of the HOSPITAL FORMULARY.

III. PROCEDURE A. FORMULARY COMMITTEE DEVELOPMENT 1. The Formulary Committee will recommend, when appropriate, amendments to this POLICY AND PROCEDURE to the Chief Medical Officer of ALPHAOMEGA HEALTH. After revisions to any of these proposed amendments by the Chief Medical Officer, in collaboration with the Formulary Committee, the Chief Medical Officer will submit the amendments to the Executive Committee of the Medical Staff at each ALPHAOMEGA HEALTH Hospital for final approval. 2. The Officers of the Formulary Committee will prepare an Annual Membership Report to the Chief Medical Officer of ALPHAOMEGA HEALTH regarding participation of its Members and any recommendations that may be important to maintain the expertise necessary for the affairs of the Formulary Committee. 3. The Officers of the Formulary Committee will prepare an Annual Report and submit it to the Professional Affairs Committee of ALPHAOMEGA HEALTH for approval. As a result of this review, the Professional Affairs Committee may make recommendations to the Formulary Committee for consideration regarding its affairs or to the Chief Medical Officer regarding amendments to this POLICY AND PROCEDURE. B. FORMULARY COMMITTEE ORGANIZATION 1. REGULAR MEMBERS a. MEDICAL STAFF MEMBERS i. There may be up to 16 Medical Staff members nominated annually by the Chief Medical Officer of ALPHAOMEGA HEALTH, each President or Chief of Staff from the Medical Staff of an ALPHAOMEGA HEALTH Hospital, or the Officers

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of the Formulary Committee. Any Medical Staff nominee must have demonstrated an active interest in evidence-based therapeutics, a willingness to be an active participant in the affairs of the Formulary Committee, and represent as a group, whenever possible, the specialties of Family Practice, Internal Medicine, Pediatrics, Obstetrics and Gynecology, Hematology and Oncology, Cardiology, Infectious Disease, Pulmonology, and General Surgery. ii. From any Nominees, 12-16 will be selected by the Chief Medical Officer of ALPHAOMEGA HEALTH on the basis of maintaining a reasonable balance among the following factors: hospital and outpatient-based physicians, primary care and disease focused physicians, physician liaison to the Medical Staff Executive Committee or P&T Committee of each ALPHAOMEGA HEALTH Hospital, and a balanced representation from the Medical Staffs of the ALPHAOMEGA HEALTH Hospitals. b. ADMINISTRATION MEMBER—The Chief Medical Officer of ALPHAOMEGA HEALTH, or designee who is a Medical Staff Member of an ALPHAOMEGA HEALTH Hospital, will be a Member of the Formulary Committee. 2. SPECIAL MEMBERS AND SOURCE OF SELECTION a. The Chief Medical Officer of ALPHAOMEGA HEALTH will select Special Members as may be needed to provide administrative or technical support for the affairs of the Formulary Committee. The Special Members will include, at a minimum: i. any pharmacist recommended by the Pharmacist in charge at a Hospital Pharmacy of ALPHAOMEGA HEALTH and ii. at least one Registered Nurse from among the Nursing Staff of an ALPHAOMEGA HEALTH Hospital. b. The Chairperson of the Formulary Committee may select one or more Special Members from the personnel of ALPHAOMEGA HEALTH or the Medical Staff of any ALPHAOMEGA HEALTH Hospital on a temporary basis as may be necessary for: i. technical support for the activities of the Formulary Committee or any Ad Hoc Subcommittee of the Formulary Committee or ii. information for the deliberations of the Formulary Committee regarding a proposal to add or delete an individual therapeutic product listed on the HOSPITAL FORMULARY. 3. FORMULARY COMMITTEE OFFICERS a. The CHAIRPERSON will be selected by the Chief Medical Officer of ALPHAOMEGA HEALTH from among the Regular Members of the Formulary Committee. The Chairperson will: i. manage the affairs of the Formulary Committee in a manner to I) support the active, positive involvement of each Regular and Special Member, II) acknowledge any conflict of interests, III) initiate a replacement appointment of any Officer, Regular Member, or Special Member becoming inactive during a calendar year,

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IV) appoint temporary Special Members, and V) select the location for Meetings of the Formulary Committee; ii. prepare the Annual Membership and Self-Evaluation reports; and iii. appoint an Ad Hoc Committee when necessary to study decisions in greater depth or to arrive at consensus recommendations for consideration by the Formulary Committee whose membership will be: I) six or less members from the Medical Staffs of the ALPHAOMEGA HEALTH Hospitals, II) at least one member who is a Regular Member of the Formulary Committee, and III) the Secretary, or designee, of the Formulary Committee. b. The VICE CHAIRPERSON will be selected by the Chief Medical Officer of ALPHAOMEGA HEALTH from the Regular Members of the Formulary Committee. The Vice chairperson will assume the duties of the Chairperson during their absence. c. The SECRETARY will be selected by the Chief Medical Officer of ALPHAOMEGA HEALTH from among the Regular or Special Members of the Formulary Committee. The Secretary will assist the Chairperson in managing the affairs of the Formulary Committee by: i. preparing the minutes for each meeting of the Formulary Committee or any of its Ad Hoc Committees, ii. sending an Agenda to the Members prior to each meeting of the Formulary Committee, iii. maintaining a schedule for the annual regular review by the Formulary Committee of all therapeutic products listed on the HOSPITAL FORMULARY, and iv. coordinating the preparation of any Drug Monograph or any other report necessary for a meeting of the Formulary Committee by a Pharmacist In Charge, or designee, at an ALPHAOMEGA HEALTH Hospital. 4. TERM OF APPOINTMENT a. The Regular and Special Members will be appointed or reappointed each January for 1 year. b. Each Officer will be appointed or reappointed each January for 1 year. 5. VOTING a. Each Regular Member will have one vote, and each Special Member will have not have a vote. b. Any two Regular Members present during a Meeting of the Formulary Committee will constitute a quorum. c. The Regular Members present at a meeting of the Formulary Committee should recognize that a decision regarding a special issue may not be appropriate if certain Regular or Special Members having expertise related to the issue are not present. Based on attendance or any other pertinent reason, the Regular Members present at a meeting of the Formulary Committee should delay making any permanent decision when the appropriate expertise is not available during a meeting of the Formulary Committee.

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d. A simple majority of Regular Members voting will be required for any action of the Formulary Committee. Any abstention on the basis of a conflict of interests will be noted in the minutes for the meeting. 6. LIAISON—A Regular or Special Member may be appointed by the Chief Medical Officer of ALPHAOMEGA HEALTH to report on the affairs of the Formulary Committee during the deliberations of any other Committee of ALPHAOMEGA HEALTH. 7. MEETINGS—Thpe meetings of the Formulary Committee will be: a. scheduled once a month for 1 hour or as may be planned by the Members of the Formulary Committee, b. attended by Regular and Special Members only, and c. convened at a location arranged by the Chairperson. 8. COMMITTEE PROTOCOLS—The Formulary Committee may also arrange for the: a. definitions applicable to the resignation and replacement of any Regular Member, Special Member, or Officer during a calendar year; b. management of any potential or actual conflict of interests affecting the participation of a Regular or Special Member during a meeting of the Formulary Committee; c. use of ALTERNATIVE MEDICATION for the health care of a patient at any ALPHAOMEGA HEALTH Hospital; d. information necessary to request a change in the list of therapeutic products or other information described in the HOSPITAL FORMULARY; e. contents of a DRUG MONOGRAPH that must be prepared before a therapeutic product not listed on the HOSPITAL FORMULARY is administered to a patient or before a therapeutic product is added to the HOSPITAL FORMULARY; and f. management of any shortage of a therapeutic product listed in the HOSPITAL FORMULARY by the: i. timely notification of the Medical Staff at each ALPHAOMEGA HEALTH Hospital listing the specific dosage forms in limited or unavailable supply, ii. development of alternative strategies for a patient’s health care using therapeutic products currently available on the HOSPITAL FORMULARY when a therapeutic product becomes either not available or in limited supply, iii. collaboration with the appropriate expertise within the Medical Staff of ALPHAOMEGA HEALTH Hospitals when a rationing protocol is necessary for a critical therapeutic product in limited supply, and iv. review of any proposal for a rationing protocol by the Ethics Council of ALPHAOMEGA HEALTH when the Formulary Committee requests assistance before final approval to ensure that the appropriate ethical standards have been considered. C. HOSPITAL FORMULARY FORMAT 1. Any therapeutic product used in the health care of a patient will be eligible for the HOSPITAL FORMULARY. This includes samples, prescription drugs as defined by the Food and Drug Administration, herbal or other alternative therapies administered topically or enterally, nutraceuticals, nonprescription drugs, vaccines, diagnostic or contrast

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agents, radioactive agents, respiratory products, parenteral or enteral nutrients, blood products, intravenous solutions, and anesthetic gases. A therapeutic product may not be considered for the HOSPITAL FORMULARY if it would normally be considered a medical device, durable medical equipment, or implant. 2. The HOSPITAL FORMULARY will list the therapeutic products approved by the Formulary Committee in a format approved by the Formulary Committee. The format for the HOSPITAL FORMULARY will reflect the recommendations of nationally recognized organizations and include certain attributes, where appropriate, as described below. a. Any restricted use provision will be defined by credentialing categories in use by the Medical Staffs of ALPHAOMEGA HEALTH Hospitals and be implemented when necessary to monitor or limit the use of a HOSPITAL FORMULARY therapeutic product known to be associated with: i. an increased risk of a substantial adverse patient reaction, ii. a highly specific therapeutic indication, or iii. an unusual impact on the overall cost of health care. b. Specific patient education provisions will be added for any HOSPITAL FORMULARY therapeutic product known to require: i. special nutritional adjustments, ii. prevention of substantial adverse effects or noncompliance, or iii. unique requirements for informed consent. c. Continuing education provisions will be added when a Medical Staff Member or qualified Hospital employee requires specialized knowledge prior to or during the administration of a given HOSPITAL FORMULARY therapeutic product. This is particularly applicable in the professional areas of oncology and cardiology. d. Special information may be added to assist the Medical Staff at each ALPHAOMEGA HEALTH HOSPITAL when necessary to improve the: i. level of compliance with prescribing only therapeutic products listed on the HOSPITAL FORMULARY, ii acceptance of rational therapeutic concepts as a basis for planning health care intervention strategies, and iii acceptance of therapeutic interchange strategies involving therapeutic products not listed on the HOSPITAL FORMULARY. 3. Each therapeutic product listed in the HOSPITAL FORMULARY will normally be stocked in each ALPHAOMEGA HEALTH Hospital’s Pharmacy. The Formulary Committee may establish an alternative provision for inventory control of a HOSPITAL FORMULARY therapeutic product when the alternative provision will not interfere with the health care of an individual patient hospitalized at an ALPHAOMEGA HEALTH Hospital. D. HOSPITAL FORMULARY MAINTENANCE 1. A proposal for a change in a single therapeutic product listed on the HOSPITAL FORMULARY will require a specific set of steps before final approval by the Formulary

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Committee. These steps are defined below. The Formulary Committee may make a temporary exception to this provision when necessary to improve the quality of health care to patients at an ALPHAOMEGA HEALTH Hospital. a. timely submission of a completed Formulary Request form to any pharmacist at an ALPHAOMEGA HEALTH Hospital by a Medical Staff member of an ALPHAOMEGA HEALTH Hospital or other professional employee of ALPHAOMEGA HEALTH, b. review of the Formulary Request by a pharmacist in charge, or designee, of an ALPHAOMEGA HEALTH Hospital’s Pharmacy to be sure that it has been fully completed, c. preparation of a Drug Monograph, as may be arranged by the Secretary of the Formulary Committee if a new therapeutic product has been proposed by the Formulary Request for the HOSPITAL FORMULARY, d. preliminary review of the Formulary Request and any associated Drug Monograph by representative specialists affected by any proposed change in the HOSPITAL FORMULARY, e. initial approval or disapproval of the Formulary Request at one meeting of the Formulary Committee, followed by review for comments at each ALPHAOMEGA HEALTH Hospital’s P&T Committee, before final approval or disapproval including any amendments to the Formulary Request at a subsequent meeting of the Formulary Committee. 2. The Formulary Committee will annually review all therapeutic products listed on the HOSPITAL FORMULARY according to a schedule of therapeutic classes as may be arranged throughout a calendar year by the Secretary of the Formulary Committee. The review of each class of therapeutic products will require a specific set of events before final approval. These steps are defined below. a. review of a class of therapeutic products preliminarily by the pharmacists in charge, or designees, of the ALPHAOMEGA HEALTH Hospital Pharmacies prior to a meeting of the Formulary Committee regarding the possible need to: i. initiate a Formulary Request for a new addition to the HOSPITAL FORMULARY, ii. deletion of a therapeutic product because of production defects, nonuse, nonavailability, recall, or replacement by another therapeutic product, or iii. a need to change information included in the HOSPITAL FORMULARY such as patient education, professional education, therapeutic interchange, or a restricted use provision; b. preliminary review of the proposed revisions to the HOSPITAL FORMULARY by representative specialists affected by the proposed revisions; c. initial approval or disapproval of the therapeutic product class review at one meeting of the Formulary Committee, followed by review for comments at each ALPHAOMEGA HEALTH Hospital’s P&T Committee, before final approval or disapproval including amendments to the class review at a subsequent meeting of the Formulary Committee. 3. The Formulary Committee may authorize certain strategies by the ALPHAOMEGA HEALTH Hospital pharmacies that are necessary to offer the most appropriate

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therapeutic products for hospitalized patients. The Formulary Committee may authorize these special strategies when supported by its own decision and the support of each ALPHAOMEGA HEALTH Hospital’s P&T Committee. Certain specific strategies to be authorized by this POLICY AND PROCEDURE are listed below. a. A class review of HOSPITAL FORMULARY therapeutic products as described above may also be initiated when there is a Formulary Request for a therapeutic product that substantially affects the inclusion or supplementary information of other therapeutic products currently listed in the HOSPITAL FORMULARY. b. The pharmacist in charge, or designee, at all ALPHAOMEGA HEALTH Hospital Pharmacies will arrange to prepare a preliminary or full Drug Monograph before any therapeutic product is dispensed that has not previously been ordered for a hospitalized patient at any ALPHAOMEGA HEALTH Hospital. c. The Formulary Committee may provide for automatic therapeutic interchange between a therapeutic product that is not listed for another therapeutic product that is listed on the HOSPITAL FORMULARY when supported by appropriate scientific evidence and appropriately considered standards of practice. d. The Formulary Committee may also select certain therapeutic products for the HOSPITAL FORMULARY that will be dispensed for certain indications or any indication even if prescribed with a “Do Not Substitute” designation. The Formulary Committee will use the same process for this designation as defined above for a new change in the HOSPITAL FORMULARY. E. HOSPITAL FORMULARY COMPLIANCE 1. The P&T Committee of each ALPHAOMEGA HEALTH Hospital will be responsible for monitoring each Medical Staff physician’s orders for a therapeutic product that is: a. not listed or does not have an automatic therapeutic interchange with a therapeutic product listed on the current HOSPITAL FORMULARY, b. for an indication not permitted by the HOSPITAL FORMULARY, or c. for an indication having a restricted use provision. 2. Any ALPHAOMEGA HEALTH Hospital’s P&T Committee may establish a Special Formulary as a means to temporarily support the efforts of its Medical Staff in the health care of hospitalized patients having special requirements that are unique to that Hospital. The Special Formulary therapeutic products will be selected using the same process defined above for a change in the HOSPITAL FORMULARY. For a Special Formulary, the other Committees of the Hospital’s Medical Staff will provide the advice and consent process. For any therapeutic product listed on an ALPHAOMEGA HEALTH Hospital’s Special Formulary for 1 year or more, continued use of the Special Formulary status for the therapeutic product will require the approval of the Formulary Committee. 3. If a P&T Committee votes to not accept a decision of the Formulary Committee, the Chairperson, or designee, of the P&T Committee will be invited to a subsequent meeting of the Formulary Committee. At this Formulary Meeting, the Formulary Committee will attempt to develop a strategy for resolving the conflict between the original decision of

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the Formulary Committee and the respective P&T Committee. In the event that a resolution is not achieved, the issue may be appealed by either Committee to the Professional Affairs Committee for a final decision within 3 months of the appeal. F. QUALITY IMPROVEMENT 1. The Formulary Committee will maintain access to the decisions of other hospital’s Formulary or P&T Committees as a resource for the basis in managing difficult decisions regarding the HOSPITAL FORMULARY. The hospitals chosen should reflect regional as well as national locations. 2. The Formulary Committee will regularly assess the pending availability of new therapeutic products in the future that will likely require the preparation of a Formulary Request and Drug Monograph. 3. The Formulary Committee will regularly monitor the possible evolution of a shortage involving the availability of a therapeutic product listed on the HOSPITAL FORMULARY. 4. The Formulary Committee may recommend to each P&T Committee certain quality improvement projects, such as a Drug Use Evaluations for a certain product that would reflect the health care at all ALPHAOMEGA HEALTH Hospitals. 5. The Formulary Committee will monitor all black box warnings or other Advisories issued by the Food and Drug Administration or pharmaceutical manufacturing company. The Formulary Committee will use the monitoring process as a basis to collaborate with each ALPHAOMEGA HEALTH Hospital’s P&T Committee as a means to promote patient safety. 6. The Formulary Committee will maintain a newsletter regarding its decisions and distribute it to each member of the Medical Staff of all ALPHAOMEGA HEALTH Hospitals. 7. The Formulary Committee will collaborate with the P&T Committee at each ALPHAOMEGA HEALTH Hospital to develop educational strategies for the ALPHAOMEGA HEALTH professional employees and each Hospital’s Medical Staff that builds support for the principles and priorities used to maintain the HOSPITAL FORMULARY. 8. The Formulary Committee will offer consultation when requested or directed by the Board of Directors of ALPHAOMEGA HEALTH, its Committees, or any other ALPHAOMEGA HEALTH Committee regarding therapeutic products in the investigation, protocols, standard order sets, or quality assessment of health care. 9. The Formulary Committee will offer a means to coordinate the standardization of POLICY AND PROCEDUREs for the Pharmacy Departments of ALPHAOMEGA HEALTH Hospitals.

POLICY TITLE: HOSPITAL PHARMACY AND THERAPEUTICS COMMITTEE I. PURPOSE To maintain a Pharmacy and Therapeutics Committee as a means to enhance the quality of health care for all patients served by the Alpha Medical Center.

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II. POLICY A. The Pharmacy and Therapeutics Committee of Alpha Medical Center will periodically evaluate its performance as a means to improve its ability to support the Vision and Mission of ALPHAOMEGA HEALTH. B. The Alpha Medical Center will maintain a Pharmacy and Therapeutics Committee (P&T committee) to implement this POLICY in accord with the applicable Medical Staff By-Laws and this POLICY. C. The P&T Committee may maintain a SPECIAL FORMULARY at the Alpha Medical Center based on the provisions of the Hospital Formulary System POLICY AND PROCEDURE of ALPHAOMEGA HEALTH. D. The P&T Committee will monitor compliance with the provisions of the HOSPITAL FORMULARY. E. The P&T Committee will support the quality improvement functions of ALPHAOMEGA HEALTH where necessary to improve the use of the HOSPITAL FORMULARY. F. The P&T Committee will review and approve any POLICY AND PROCEDURE of the Alpha Medical Center Pharmacy.

III. PROCEDURE A. PHARMACY AND THERAPEUTICS COMMITTEE DEVELOPMENT 1. The P&T Committee will recommend, when appropriate, amendments to this POLICY AND PROCEDURE to the Administrator of Alpha Medical Center. After revisions to any of these proposed amendments by the Administrator, in collaboration with the P&T Committee, the Administrator will submit the amendments to the Executive Committee of the Alpha Medical Center Medical Staff for final approval. 2. The Officers of the P&T Committee will prepare an Annual Membership Report to the Administrator of the Alpha Medical Center regarding participation of its Members and any recommendations for changes in its membership that may be important to maintain the expertise necessary for the affairs of the P&T Committee. 3. The Officers of the P&T Committee will prepare an Annual Report and submit it to the Executive Committee of the Alpha Medical Center Medical Staff for approval. As a result of this review, the Executive Committee may make recommendations to the P&T Committee for consideration regarding its affairs or to the Administrator regarding amendments to this POLICY AND PROCEDURE. B. FORMULARY COMMITTEE ORGANIZATION 1. REGULAR MEMBERS AND SOURCE OF SELECTION a. MEDICAL STAFF MEMBERS i There may be up to eight Medical Staff members nominated annually by the Administrator, or designee, of Alpha Medical Center, the President of the Medical Staff of the Alpha Medical Center, or the Officers of the P&T Committee. Any Medical Staff nominee must have demonstrated an active interest in evidencebased therapeutics, a willingness to be an active participant in the affairs of the P&T Committee, and represent as a group, whenever possible, the specialties of

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Family Practice, Internal Medicine, Pediatrics, Obstetrics and Gynecology, Hematology and Oncology, Cardiology, Infectious Disease, Pulmonology, and General Surgery. ii From any nominees, eight will be selected by the Administrator, or designee, of Alpha Medical Center on the basis of maintaining a reasonable balance among the following factors: hospital and outpatient-based physicians, primary care and disease focused physicians, physician continuity from year to year, and physician liaison to the Medical Staff Executive Committee of the Alpha Medical Center or the Formulary Committee of ALPHAOMEGA HEALTH. b. PHARMACY MEMBERS—The Administrator, or designee, of Alpha Medical Center will select two pharmacists that will include the Pharmacist In Charge of the Hospital’s Pharmacy. c. NURSING SERVICE MEMBER—The Administrator, or designee, of Alpha Medical Center will select one registered nurse from the Nursing Service. 2. SPECIAL MEMBERS AND SOURCE OF SELECTION a. The Administrator, or designee, of Alpha Medical Center may select Special Members as needed to provide administrative or technical support for the affairs of the P&T Committee. b. The Chairperson of the Formulary Committee may select one or more Special Members from the personnel of the Alpha Medical Center or its Medical Staff on a temporary basis as may be necessary for: i. technical support for the activities of the P&T Committee or any Ad Hoc Subcommittee or ii. information for the deliberations of the P&T Committee regarding a proposal to add or delete an individual therapeutic product listed on the HOSPITAL FORMULARY. 3. P&T COMMITTEE OFFICERS AND SOURCE OF SELECTION a. The CHAIRPERSON will be selected by the Administrator, or designee, of the Alpha Medical Center from the physician Regular Members of the P&T Committee. The Chairperson will: i. manage the affairs of the P&T Committee in a manner to: I) support the active, positive involvement of each Regular and Special Member, II) acknowledge any conflict of interests, III) initiate a replacement appointment of any Officer, Regular Member, or Special Member becoming inactive during a calendar year, IV) appoint temporary Special Members, V) select the location for Meetings of the P&T Committee; ii. prepare the Annual Membership and Self-Evaluation reports; and iii. appoint an Ad Hoc Committee when necessary to study decisions in greater depth or to arrive at consensus recommendations for consideration by the P&T Committee whose membership will be:

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4.

5.

6.

7.

I) six or less members from the Medical Staff of the Alpha Medical Center, II) at least one member who is a physician Regular Member of the P&T Committee, and III) the Secretary, or designee, of the P&T Committee. b. The VICE CHAIRPERSON will be selected by the Administrator of the Alpha Medical Center from among the physician Regular Members of the P&T Committee. The Vice Chairperson will assume the duties of the Chairperson during their absence. c. The SECRETARY will be selected by the Administrator of the Alpha Medical Center from among the Regular or Special Members of the P&T Committee. The Secretary will assist the Chairperson in managing the affairs of the P&T Committee by: i. preparing the minutes for each meeting of the P&T Committee or any of its Ad Hoc Committees, ii. sending an Agenda to the Members prior to each meeting of the P&T Committee, iii. maintaining liaison with the other Committees of the Medical Staff, iv. maintaining a schedule for the annual Quality Assurance activities of the P&T Committee, and v. assisting in the preparation of any Drug Monograph or any other report necessary for a meeting of the Formulary Committee of ALPHAOMEGA HEALTH. TERM OF APPOINTMENT a. The Regular and Special Members will be appointed or reappointed each January for 1 year. b. Each Officer will be appointed or reappointed each January for 1 year. VOTING a. Each Regular Member will have one vote, and each Special Member will have not have a vote. b. Any two physician Regular Members present during a Meeting of the Formulary Committee will constitute a quorum. c. The Regular Members present at a meeting of the P&T Committee should recognize that a decision regarding a special issue may not be appropriate if certain Regular or Special Members having expertise related to the issue are not present. Based on attendance or any other pertinent reason, the Regular Members present at a meeting of the P&T Committee should delay making any permanent decision when the appropriate expertise is not available during a meeting of the P&T Committee. d. A simple majority of Regular Members voting will be required for any action of the P&T Committee. Any abstention on the basis of a conflict of interests will be noted in the Minutes for the meeting. LIAISON—A Regular or Special Member may be appointed by the Administrator to report on the affairs of the P&T Committee during the deliberations of any other Committee of the Alpha Medical Center. MEETINGS—The meetings of the P&T Committee will be: a. scheduled once a month for 1 hour or as may be planned by the Members of the P&T Committee,

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b. attended by Regular and Special Members only, c. convened at a location arranged by the Chairperson. 8. COMMITTEE PROTOCOLS—The P&T Committee may also arrange for the: a. use of definitions applicable to the resignation and replacement of any Regular Member, Special Member, or Officer during a calendar year as may be established by the Formulary Committee of ALPHAOMEGA HEALTH and b. management of any potential or actual conflict of interests affecting the participation of a Regular or Special Member during a meeting of the P&T Committee as may be determined by the Formulary Committee of ALPHAOMEGA HEALTH. C. HOSPITAL FORMULARY DEVELOPMENT 1. The P&T Committee will review for comment at each meeting any therapeutic product recommended for addition or deletion to the HOSPITAL FORMULARY by the Formulary Committee of ALPHAOMEGA HEALTH. 2. The P&T Committee will review for comment at each meeting any class review of therapeutic products by the Formulary Committee of ALPHAOMEGA HEALTH and their recommendations for changes in the HOSPITAL FORMULARY. D. HOSPITAL FORMULARY COMPLIANCE 1. The P&T Committee will monitor each Medical Staff physician’s orders for a therapeutic product that is: a. not listed or does not have an automatic therapeutic interchange with a therapeutic product listed on the current HOSPITAL FORMULARY, b. for an indication not permitted by the HOSPITAL FORMULARY, or c. for an indication having a restricted use provision. 2. The P&T Committee may establish a Special Formulary for therapeutic products not listed on the HOSPITAL FORMULARY as a means to temporarily support the efforts of the Medical Staff for hospitalized patients having special requirements that are unique to Alpha Medical Center. The Special Formulary therapeutic products will be selected using the same process defined by the ALPHAOMEGA HEALTH Formulary Committee for the HOSPITAL FORMULARY. For a Special Formulary, the other Committees of the Alpha Medical Center’s Medical Staff will provide the advice and consent process. For any therapeutic product listed on Special Formulary for 1 year or more, continued use of the Special Formulary status for the therapeutic product will require the approval of the Formulary Committee. 3. If the Alpha Medical Center P&T Committee votes to not accept a decision of the Formulary Committee, the Chairperson, or designee, of the P&T Committee will attend a subsequent meeting of the Formulary Committee. At this Formulary Meeting, the Formulary Committee will attempt to develop a strategy for resolving the conflict between the original decision of the Formulary Committee and the P&T Committee of the Alpha Medical Center. In the event that a resolution is not achieved, the issue may be appealed by either the Formulary Committee or the Alpha Medical Center P&T Committee to the Professional Affairs Committee for a final decision within 3 months of the appeal.

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E. QUALITY IMPROVEMENT 1. The P&T Committee will regularly review the decisions of the ALPHAOMEGA HEALTH Formulary Committee as a means to evaluate any issues requiring the development of carefully considered implementation requirements at the Alpha Medical Center, such as the shortage of a therapeutic product. 2. The P&T Committee will maintain an annually revised schedule for Drug Use Evaluations as may be established through consultation with other Medical Staff Committees. 3. The P&T Committee or an Ad Hoc Committee will review all Medication Error Reports. 4. The P&T Committee will quarterly review all Adverse Medication Reaction Reports. 5. The P&T Committee will participate in the development of standard order sets as may be requested by a Member, a group of Members, or a Committee of the Medical Staff. Generally, the P&T Committee will not have primary responsibility of a standard order set unless specifically requested by the Executive Committee of the Medical Staff. 6. The P&T Committee will prepare an annual report to the Executive Committee regarding the overall level of prescribing compliance with the HOSPITAL FORMULARY. 7. The P&T Committee in collaboration with the Formulary Committee will monitor all black box warnings or other advisories issued by the Food and Drug Administration or pharmaceutical manufacturing company. The Formulary Committee will use the monitoring process as a basis to collaborate with each P&T Committee of ALPHAOMEGA HEALTH as a means to promote patient safety. 8. The P&T Committee will suggest information to the Formulary Committee for inclusion in the HOSPITAL FORMULARY newsletter. 9. The P&T Committee may make recommendations to the Medical Staff of Alpha Medical Center regarding the health care of hospitalized patients regarding the use of the HOSPITAL FORMULARY based on the outcome of certain studies undertaken by the P&T Committee. These studies will exclude any direct identification of patient names or medical records. F. PHARMACY DEPARTMENT POLICY AND PROCEDURE 1. The P&T Committee will periodically review and approve the POLICY AND PROCEDURES of the Alpha Medical Center Pharmacy Department. 2. The review and approval will be, whenever possible, coordinated with the operational statements of the other Pharmacy Departments of ALPHAOMEGA HEALTH Hospitals.

12–2 Appendix 12–2

Formulary Request Form PHARMACY AND THERAPEUTICS COMMITTEE FORMULARY ADDITION REQUEST NOTE: Both sides of this form must be completed in order for consideration by the Formulary Committee at its next regularly scheduled meeting. You may submit additional information based on the outline of this request if more space is required. If you are not a member of the committee, you must also complete a Conflict of Interest Statement and attach it to this request. Generic Name Brand Name Indications - Describe the FDA-approved or potential off-label uses which have prompted this request.

Dosing - Describe the specific strength and administration form of this product necessary for this request.

Comparative Efficacy - Describe how this agent relates to other products in terms of effectiveness.

Contraindications and Warnings - Describe any substantial issues related to this product.

Adverse Effects - List any substantial issues related to this product.

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Expected Outcomes - Describe how this product would substitute or add to the current Formulary products.

Cost of Therapy - Describe how this product would change the overall cost of medical care.

Impact on Inpatient Care Processes - Describe any special requirements on the hospital for use of this product such as nursing/medical staff education, standards of care, discharge planning, certification, or standard order sets.

Impact on Outpatient Care Processes - Describe any special requirements on ambulatory care for use of this product such as compliance, follow-up, or monitoring.

Other Considerations - Describe any information not applicable to the above categories.

Requested By - Must be a Formulary Committee Member or Hospital Medical Staff Member. Printed Name Signature Response - For record keeping by the Formulary Committee. Received by a Formulary Committee Member date Initial Formulary Committee consideration date Final Formulary Committee consifderation date Action Taken Notification of Medical Staff Member submitting request date

12–3 Appendix 12–3

P&T Committee Meeting Attributes1-3 I. TIMING

A. Regular—The choice is often between monthly or bimonthly. Overall, a long-term commitment to one schedule that does not vary is ideal. An atypical but practical variation might include monthly meetings except August and December, in order to adjust for times when it is difficult to get quorum because of vacations and holidays. To support a regular meeting cycle, any cancellation on a sudden, unexpected basis must be avoided virtually without exception. Finally, a 2- to 3-year experience with a given schedule would be necessary to permit members an opportunity to work membership commitment into their own schedule. B. Monthly work cycle—Virtually all holidays occur in association with the first or last week of any month during the calendar year. Similarly, Mondays and Fridays frequently have distractions caused by these associated weekend demands. Thus, the second or third Tuesday-Wednesday-Thursday of the calendar month are often the best choice for a regular meeting. C. Daily work cycle—Given the character of the discussion above, the start of the morning or afternoon would be ideal for a meeting. The afternoon timing could be associated with a light lunch prior to starting the meeting. II. MEETING ROOM CHARACTER

A. Location—A location that minimizes the travel barriers encountered by all the members of the committee is best. In a multihospital organization, this choice may not be ideal if a perception of interhospital territoriality would create a perception of bias in the decisions of the committee. There have also been suggestions regarding the use of teleconferencing.4 As this becomes a more widely accepted professional tool in the future, the barriers of travel time could be eliminated as a means to incorporate a higher degree of expertise within the members of the committee. B. Size—The room should have a rectangular table, or tables set up in a U shape if there are too many members for a single table, with chairs on all sides and enough room for additional chairs next to the walls for guests who might be attending a meeting. The room should allow a comfortable fit for a table that is large enough for the usual attendance as well as appropriate audiovisual equipment. Overall, the room or table should not be so

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large that the usual attendees might feel isolated and thus less engaged in the agenda of any meeting. Similarly, a full turnout would crowd the room, giving greater emphasis to the character of the deliberations. C. Seating—This can be highly defined as is seen in cases with assigned seats having a name card displayed on the table for each member. The benefits of universal identity of the members would thus be enhanced, especially if they are generally unknown to each other because of the size of an institution or hospital group. More commonly, there could be no fixed seating arrangements for a more informal tradition that could better support collaboration and open discussion. A decision by the chairperson to sit in different locations would further emphasize this approach to a seating tradition. It is also often good for pharmacy personnel to disperse themselves throughout the room to avoid a feeling of us/them in discussions.

REFERENCES 1. Doyle M, Straus D. How to make meetings work: the new interaction method. New York: Berkeley Publishing Group; 1993. 2. Nair KV, Coombs JH, Ascione FJ. Assessing the structure, activities, and functioning of P&T committees: a multisite case study. P&T. 2000;25(10):516-28. 3. Balu S, O’Connor P, Vogenberg FR. Contemporary issues affecting P&T committees. Part 2: beyond managed care. P&T. 2004;29:780-3. 4. Boedeker B. Virtual pharmacy & therapeutics meetings. The Harry S. Truman VA Hospital experience. Columbia (MO): Harry S. Truman Memorial Veteran’s Hospital; 1999 Mar [cited 2004 Jan 27]. Available from: http://www.gasnet.org/esia/1999/march/virtual.html

12–4 Appendix 12–4

Example P&T Committee Minutes ORGANIZATION, INC. PHARMACY AND THERAPEUTICS COMMITTEE MEETING January 21, 20XX SCHEDULED AT 0700 THESE MINUTES ARE PRIVILEGED AND NOT SUBJECT TO DISCLOSURE OR LEGAL DISCOVERY PROCEEDINGS UNDER (STATUTE NUMBER) I. Call to Order. The members or Guests present or members absent are indicated below (legal names, usually with degrees). The meeting was called to order by the chairperson at 7:00 AM. The physician members present represented a quorum. The minutes for the previous meeting were presented to the members. The section regarding a report of the chairperson from a discussion with the executive committee about ineligible handwriting and unapproved abbreviations was specifically reviewed by the chairperson. The minutes did not describe the executive committee’s request that the P&T committee quarterly forward five to eight examples of physician progress notes that reflect these two issues. The executive committee decided to have the president of the medical staff have individual contact with the medical staff members involved. A motion was made to approve the amended minutes and seconded. There being no further discussion, the motion was approved unanimously. After the vote, there was a brief discussion of the impending transition to a total electronic medical record with physician order entry and its ability to reduce transcribing errors. The physician members expressed concern regarding the ease of order entry. No further action was taken. II Pharmacy and Therapeutics Committee Organizational Affairs A. Policy and Procedure Amendments—The chairperson submitted a draft revision of the entire policy and procedure for the P&T committee in response to new standards of TJC and previously discussed requirements for the functions of the committee. The committee reviewed the proposed draft and agreed informally to reconsider it at the next meeting after the chairperson has had a chance to meet with the Chief Medical Officer regarding any other amendments that may be necessary. B. Committee Procedures 1. Conflict of Interest Disclosure—The chairperson gave the Members the forms neces-

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IV.

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sary to declare any potential or actual conflicts of interest according to the procedure established previously by the committee. The chairperson briefly reviewed this process and emphasized that conflicts of interest were only unacceptable when not acknowledged or no action is taken to resolve them during a meeting of the committee. 2. Formulary Request format—no change 3. Alternate Medication Use—no change 4. Drug Monograph—no change C. Committee Membership—no action; end of year report due December 31st D. Annual Report—draft Report due January 5th E. Ad Hoc Committees—none currently F. Budget—reports due February, May, August, November Formulary System A. Formulary Maintenance 1. Formulary Additions/Deletions a. IV lansoprazole (Prevacid®) b. fondaparinux (Arixtra®) c. escitalopram (Lexapro®) 2. Formulary Class Reviews 28:04 General anesthetic agents 72:00 Local anesthetic agent 86:00 Smooth muscle relaxants 24:00 Cardiovascular agents 3. Nonformulary Usage Report 4. Review of Standard Order Sets/Guidelines TPN order sheet Drug Use and Quality Improvement A. Medication Error Report—no report B. Adverse Medication Reaction Report—no report C. Drug Usage Evaluation Report—no report D. Medication Recall—no report Hospital Pharmacy Policies—no report Current Medication Shortages

12–5 Appendix 12–5

Chairperson Skills I.Experience A. KNOWLEDGE OF FORMULARY ISSUES This occurs ideally as a result of prior experience on the committee for several years. P&T committee meetings are often associated with an individual hospital, group of hospitals, a staff model health maintenance organization, or an insurance-related pharmacy benefit management (PBM) process. A chairperson’s experience in each of these areas would be ideal.

B. PROFESSIONAL PRACTICE It could be suggested that at least 10 years are required for a pharmacist, nurse, administrator, or physician to have a sense of the overall trends evolving within health care. Within a P&T committee, the chairperson would need this background to best respond to the biases that each member might bring to the deliberations. It is beneficial if the members have had mutual experience with the chairperson at a direct patient care level.

C. LEADERSHIP The chairperson is likely to be the most essential person for the overall success of a P&T committee. This is most directly related to the organization truism that it is nearly impossible to hold a committee responsible for anything except when a committee is acting as the ultimate authority for an organization. Thus, the value of a P&T committee is related to its ability to serve the common interests of the entire organization affected by its actions. If the costs of the P&T committee members’ time are considered, the committee’s activities are the result of a very expensive effort. To best utilize this expertise, a chairperson must be skilled at mobilizing these resources in a manner that bests supports the overall efforts of the organization to which it is attached. A previously demonstrated ability to create this role for a committee is the most valuable attribute for use in choosing a committee’s chairperson.

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II. Meeting Strategies A. PUNCTUALITY Given the busy schedules of the members, it is necessary to start and end on time. To open a meeting, it is best to lay out the agenda including any new additions and briefly discuss any items that will require a special discussion. Within 2 to 3 minutes, the chairperson and each member should have an understanding of the scope of the meeting ahead.

B. FAIRNESS Often the health care process vacillates unpredictably between deductive and inductive reasoning processes. External observers are often baffled by this interplay. Related to this, it is suggested that a strict use of the Robert’s Rules of Order for a meeting agenda may not facilitate the spontaneity for a committee’s members that usually underlies their involvement in the character of health care. It is the responsibility of the chairperson to guide this process and seek out the opinions that the members have for a given issue. Also, if the knowledge necessary to make the best judgment for a given issue does not exist for a decision on the issue, it is important that the chairperson be able to facilitate a consensus that develops a means to rectify the deficiency.

C. INVOLVEMENT Some members may not normally wish to participate spontaneously during a meeting. It is up to the chairperson to ask these members a specific question that would allow them a meaningful opportunity to participate in a given discussion. Occasionally, the chairperson might ask each member present about their opinion for a final decision being faced by the committee. This strategy should begin at one place around the table moving to each member present clockwise around the meeting room.

12–6 Appendix 12–6

Conflict-of-Interest Declaration FORMULARY ADDITION REQUEST CONFLICT OF INTEREST STATEMENT NOTE: This must be submitted along with the actual Request form if the person submitting the Request is not a member of the Formulary Committee. A copy of the Formulary Committee’s Policy on Conflict of Interest Management is attached. Generic Name Trade Name Substantial Involvement with a Competing Organization - Yes No Please describe if: 1) A member of a health insurance company or another health system Pharmacy and Therapeutics Committee. 2) Another health system medical staff officer. 3) A member of a group practice primarily affiliated with another health system. Substantial Involvement with a Company which Manufactures the Product or Competes with the Product’s Company - Yes No Please describe if: 1) Receiving financial income or support in the last 12 months of more than $100 for research, attendance at a company supported seminar, travel to an out-of-town meeting, or participation in a company sponsored speaker’s bureau. 2) Receiving pharmaceutical products from the company in the last 12 months for personal or family use, gifts for family or personal use, or samples for use other than as a courtesy for patients. 3) Maintaining in the last 12 months a substantial ownership of stock (>10% of outstanding shares) in the company having >30% of its revenue from sales to this organization, its affiliated organizations, or another local health system. Substantial Inside Information - Yes No Please describe if there are other outside relationships for which involvement in this request may be actually or potentially perceived as affecting the decision of the committee such as: 1) Having a substantial position of authority in another organization which might affect a member of the committee for employment or medical staff privileges.

1145

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

2) Disclosing information about this request to another organization directly or indirectly which might give this organization, the other organization, or the requester an unfair advantage. 3) Receiving substantial assistance from the company or its representative that manufactures the requested product in the preparation of this Formulary Addition Request.

13–1 Appendix 13–1

Format for Drug Monograph INSTITUTION NAME HEADING Generic Name: Can include other common, nonofficial names, e.g., TPA for alteplase. Trade Brand Name: If more than one, indicate company that each is from. Manufacturer (or source of supply): Include Web site address. Therapeutic Category: For example, Thromobolytic Agent for alteplase Classification: Note—other classifications, such as the VA Class, can also be used. • AHFS Number and Classification: If not in the book yet, see the list in the front of the AHFS Drug Information book and determine the most appropriate classification. • FDA Classification: Include specific FDA Web site URL concerning approval. • Status: Prescription, Nonprescription, and/or Controlled Substance Schedule (if applicable). Similar agents: A list of common treatments used for the same indication(s). Summary: Includes a short summary of advantages and disadvantages of the drug, particularly in relation to other drugs or treatments used for each major indication, and any other significant information. Must include indications allowed in the institution. Recommendations: Indicate whether or not the drug should be added to the drug formulary of an institution, including specifying the indications that it is approved for use in the institution, assuming they would have patients that would be treated for illnesses where this drug might be used. Also indicate specific formulary status for the drug (i.e., uncontrolled, monitored, restricted, conditional—see ASHP guidelines) and whether the drug will replace any other product that might already be on the formulary. In addition, include any information on how the drug is to be placed in any clinical guidelines. For third-party payer monographs, information will need to be included on the payment tier. Page one of the drug monograph consists of the above information

1147

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

Pharmacological Data: • Mechanism of Action (usually brief) • Bacterial Spectrum (if applicable)

Therapeutic Indications: • FDA-Approved Indications (see package insert)—Clearly state which indications are FDA

approved. Potential Unlabeled Uses (list only if they are considered to be acceptable medical practice, • although it is allowable to mention others that are early in investigation with a statement that the drug should not be used for them or that they require more study)—Clearly indicate they are not FDA approved. • How the drug, and similar drugs, fit into clinical guidelines. • Clinical Comparison (abstract at least two studies; see Appendix 9–2 for more guidelines. Include human efficacy studies and, where available, studies comparing the product to standard therapy. Note: If there are other supportive studies for an indication, they can be covered briefly, if you desire, along with the major study covered in detail. Be sure to note any deficiencies in the studies). Also, pharmacogenomic information may need to be included here and elsewhere.

Bioavailability/Pharmacokinetics: A table summarizing the following, in comparison to the gold standard, can be very useful. • Absorption • Distribution • Metabolism • Excretion

Dosage Forms: • Forms and Strengths—Compare to other agents (consider a table), since new products often

have a limited number of dosage forms/routes as compared to established products. Purity and composition information should be included for herbal and alternative medications • Explain any special information needed for preparation and storage, in comparison to other products. Sometimes a product will be so difficult to prepare or have such a limited shelf-life after preparation that it is not worth stocking

Dosage Range: • Adults • Children • Elderly • Renal or Hepatic Failure • Special Administration Requirements • Any anticipated problems in supplies (i.e., shortages) or restrictions in distribution (e.g., pre-

scriber certification required)

Known Adverse Effects/Toxicities: • Frequency and Type (A table comparing the drug to others can be a clear and concise way of

expressing this information.)

APPENDIX 131

1149

• Prevention of Toxicity • Risk and Benefit Data

Special Precautions: Usually includes pregnancy and lactation. Contraindications: List of contraindications Drug Interactions: A simple one- or two-sentence statement for each—usually separate various interactions into separate short paragraphs and compare to other drugs. • Drug–Drug • Drug–Food • Drug–Laboratory

Patient Safety Information: Includes medication error information and product safety information from outside sources (e.g., ISMP, MedWatch, FDA Patient Safety News, United States Pharmacopeia Patient Safety Program)

Patient Monitoring Guidelines: Include effectiveness, adverse effects, compliance and other appropriate items

Patient Information: • Name and description of the medication • Dosage form • Route of administration • Duration of therapy • Special directions and precautions • Side effects • Techniques for self-monitoring • Proper storage • Refill information • What to do if dose is missed

Cost Comparison: Use AWP and institutional prices, and make sure there is a comparison with any similar products at equivalent doses—a pharmacoeconomic analysis (see Chapter 6) is the best method of comparing drugs in this section; remember to include any required concomitant therapy. Providing a spreadsheet file with information to consider different patient circumstances that can change may be helpful.

Date Presented to pharmacy and therapeutics committee, and name and title of the person preparing the document References: Follow guidelines as described in Appendix 9–3.

13–2 Appendix 13–2

Example Drug Monograph Note: This example is based on fictional products and is condensed. It shows examples of most sections in a real drug monograph, but often does not go into all of the details (e.g., a table of adverse effects is seen, but only a couple items are listed, whereas a full drug monograph would list at least all common and/or serious reactions). St. Anywhere Medical Center (St. AMC) Pharmacy & Therapeutics Committee Drug Evaluation Monograph Generic Name:

artiblood

Brand Name:

MegaBlood

Manufacturer:

MegaPharmics

Therapeutic Category:

Blood substitute

Classification:

AHFS 16:00 Blood Derivatives FDA Classification: 1A Status: Prescription Only

Similar Agents:

fakered

Summary: Artiblood is a new perfluorocarbon that has many similarities to the only other product in its class, fakered. Both products have the ability to temporarily replace the oxygen-carrying function of red blood cells in patients in whom use of whole blood or packed red blood cells is impossible due to medical or religious reasons. In general, artiblood was found to be more efficacious than fakered; however, it also has been shown to produce a greater number of adverse effects. The adverse effects are mostly gastrointestinal in nature; however, the increased INR can be a problem in some patients. Artiblood is not metabolized in the body, whereas fakered is approximately 50% metabolized to inactive components. These differences are generally not clinically significant, since the dose of either product is unlikely to need adjustment. Fakered is available in several different volume bags, allowing the dose to be matched more closely to the anticipated patient need. While the cost of fakered appears to be lower, a pharmacoeconomic analysis shows that artiblood would produce the greatest cost savings for the institution.

Recommendations: It is recommended that artiblood be added to the Drug Formulary for use restricted to those who cannot use natural blood replacement products because of religious reasons or because suitable blood types are not available, including for use in cardiac catheterization procedures. It is not approved for use as a volume expander, except when in conjunction with the previous indications.

1150

APPENDIX 132

1151

Pharmacological Data: Artiblood is a type of perfluorocarbon, similar to fakered. These products have the unique ability to freely bind with or give up oxygen, depending on the partial pressures of the gas where the product is located (i.e., in the lungs there is an abundance of oxygen, so the product adsorbs oxygen; in the tissues there is a relative deficiency of oxygen, so the product gives up the gas).1,2 The products do not have direct immunologic properties, nor do they have the ability to aid in blood clotting, although there may be some effect on blood clotting (either interference by coating platelets or precipitation of the clotting pathway mechanism).3 In addition to oxygen-carrying capabilities, the products have some plasma volume expansion properties. Artiblood has a similar effect to Dextran 40,1 whereas fakered’s properties are relatively insignificant.4 Maximum plasma volume expansion occurs within several minutes of administration and lasts for approximately 1 day in normal patients. This results in increased central venous pressure, cardiac output, stroke volume, blood pressure, urinary output, capillary perfusion, and pulse pressure. Microcirculation is improved.

Therapeutic Indications: Indications: Artiblood is FDA approved for the short-term replacement of the oxygen-carrying capabilities of blood in patients who cannot use normal whole blood.1 In addition, the product has been used successfully in cardiac catheter procedures, although this use is not FDA approved.5 There is some early research into the use of the product as a plasma expansion product, but there is not enough information to support this use.6 Fakered is approved only for use in cardiac catheterization,2 although it is commonly used as a blood replacement product in patients who cannot or will not use whole blood products.7

Evidence-Based Clinical Guidelines: A search of the literature was performed to identify evidence-based clinical guidelines. This included Medline, Embase Drugs and Pharmacology, the National Guideline Clearinghouse Web site, the American College of Cardiology Web site, and approximately a dozen Internet search engines; however, no applicable guidelines were identified.

Clinical Studies: Max and Sugar6 conducted a comparison trial of artiblood (500 mL/day administered once daily to over 1 hour to 80 patients) and fakered (750 mL administered once over 90 minutes to 82 patients) in patients (18–80 years of age) suffering from massive blood loss (>1 L), who could not use whole blood due to religious beliefs (e.g., Jehovah’s Witnesses). In the artiblood group, all patients were undergoing openheart surgery, as were 78 of the patients in fakered group. The remainder of the fakered group consisted of gunshot patients. Patients with renal insufficiency (creatinine clearance < 50 mL/min) or diagnosed with liver dysfunction were eliminated from consideration. Both groups were similar, except that the artiblood group had more smokers, which may have had an effect on oxygen requirements. Withdrawals from the artiblood group were for the following reasons: death due to failure of heart-lung machine (one patient), noncompliance with protocol (10 patients), worsening symptoms (three patients), and side effects (one patient—vomiting). The authors noted that protocol compliance problems were due to

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

inappropriate staff education and were not related to the drug itself. In the fakered group, withdrawals were due to side effects one patient, diarrhea; one patient, nausea; one patient, abdominal cramps) and noncompliance with protocol (two patients). The patients were assessed on the following items: oxygen and carbon dioxide content of the blood (samples drawn immediately before and after administration, and every 4 hours for 24 hours), coagulation profile of patient (drawn within 2 hours before and after administration), effect on normal blood chemistry profiles (SMA-20) (drawn within 2 hours before and after administration), and time to discontinuation of supplemental oxygen to the patient. Adverse effects were also noted. Results were analyzed using appropriate statistical methods. Artiblood was found to increase the oxygen-carrying capabilities of the blood in comparison to fakered (p < 0.01), although fakered did significantly improve oxygen-carrying capabilities over baseline (p < 0.05). While fakered had minimal effect on blood chemistry and coagulation profile, it was noted that INRs were increased in patients receiving artiblood (p < 0.001). Other adverse effects, mostly gastrointestinal in nature, were more common with fakered, although the symptoms typically disappeared within 2 hours of administration. Other measured characteristics seemed similar between the two groups. The authors concluded that artiblood was the superior agent, due to increased oxygen-carrying capabilities. The authors downplayed adverse effects, although the effects on INRs do appear worrisome. [Other studies would be covered here for all likely uses within an institution.] There were no studies found that demonstrated any effects of genome on either artiblood or fakered therapy.

Bioavailability/Pharmacokinetics16–18: Absorption: Absorption is not applicable, since these agents are administered by IV infusion.

Distribution: Artiblood is found in the blood stream, with little being distributed to the tissues. Approximately 5% of fakered is found in the liver, with the rest being in the bloodstream.

Metabolism: Artiblood is not metabolized in the body, whereas approximately 50% of fakered is broken down to inactive components and is excreted in the bile.

Elimination: Artiblood has a half-life of 5 to 15 hours. It is excreted unchanged in the urine. The longer half-life is seen in patients with renal insufficiency. Since the drug is usually given as a single dose, renal insufficiency does not pose a significant problem. Fakered has a half-life of 4 to 7 hours in normal patients. Significant renal or hepatic impairment may double the half-life.

Dosage Forms: Large Volume Parenteral: • Artiblood—500mL IV bags • Fakered—500, 750, and 1000 mL IV bags

No other forms or strengths available. This product will have limited availability for the next 6 months due to the ability of the manufacturer to produce an adequate amount to satisfy demands.

APPENDIX 132

1153

No problems in availability are expected after that point. Due to the restrictions on indicated uses in the institution, this is not expected to cause any difficulties and, therefore, no specific procedures are being mandated to address a possible shortage.

Dosage Range: The normal dose of artiblood for blood replacement is 500 mL, which may be repeated once after 4 hours. Doses may be cut in half for patients weighing less than 50 kg. No dosage adjustments are necessary in renal or hepatic impairment. The product has not been tested in patients younger than 12 years of age and is not recommended in that population. No dosage adjustment is necessary in the elderly.1 Fakered is given in doses of 500 mL to 1 L, with a maximum daily dose of 1.5 L. The dose is adjusted based on clinical response of the patient. The product can be used in patients as young as 6 years of age; however, the initial dose is 250 mL.2

Known Adverse Effects/Toxicities: The two agents are compared in the following table: Adverse Effect

Artiblood (% of patients)

Fakered (% of patients)

Nausea

20

7

…

…

…

Gastrointestinal

Special Precautions: Neither drug has been studied long term; therefore, the effects are not known. Both products are considered Pregnancy Category C. Tests in pregnant animals have shown adverse effects and no adequate, well-controlled studies have been conducted in humans. There is no information available on the excretion of the drug in human milk. Overall, when considering use in pregnant or lactating women, the physician must consider the benefits versus the risks. Safety and effectiveness of artiblood in children have not been established, although fakered may be used in children at least 6 years old.

Contraindications: Both agents are contraindicated in patients with hypersensitivities to the drug or any component of the dosage form.

Drug Interactions: Drug–Drug Interactions: Heparin—Effects of heparin or low-molecular weight heparins may be significantly increased by either artificial blood replacement agent, although the effect by artiblood tends to be greater. There is no effect on either artiblood or fakered, although the heparin may improve circulation of the products to underperfused tissues. (Other interactions for both drugs would be listed and compared.)

Drug–Food Interactions: None are known or expected, since these agents are given intravenously and do not undergo enterohepatic recirculation.

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

Drug–Laboratory Test Interactions: INR—INRs can be increased by both agents, although the effect is more noticeable with artiblood. (Other interactions for both drugs would be listed and compared.)

Patient Safety: This product has a good patient safety profile, with relatively minor adverse effects (e.g., nausea). Since the product has no coagulation or immunologic activity, health care providers must be aware that it is only used for temporary help in oxygen-carrying capabilities. Other specific safety concerns include: • Patients on warfarin must have a baseline INR and one each day for the 2 days following

administration. • The product has been on the market less than 6 months and information is limited. • Product must be refrigerated until approximately 30 minutes prior to infusion.

Patient Monitoring Guidelines: Monitor patient for objective evidence of effectiveness (e.g., oxygen content of blood and clinical effects). Obtain baseline INR and normal chemistry values, and monitor regularly. Monitor for adverse effects.

Patient Information: In a patient receiving the product due to trauma, it is likely that he or she will not be able to be given information. In that case, provide the information to the next of kin or guardian. Inform patients that the product is an intravenous product that does not contain any blood products. The patient or family should know that he or she may receive this product once or more during the first day after surgery. The patient or family should be informed that the drug has few noticeable adverse effects other than some gastrointestinal upset; however, the physician or pharmacist should be consulted if anything unusual occurs. The patient or family should know that some blood tests will be regularly performed to exclude the possibility of adverse effects. The nurse will keep the drug refrigerated until approximately 30 minutes before infusion. Warnings about missed doses are irrelevant.

Cost Comparison: General pricing information: AWP

Daily Dose*

St. AMC

Daily Dose*

Artiblood 500 mL

$2500/bag

$2500

$2310/bag

$2310

Fakered 500 mL

$1000/bag

$1000

$800/bag

$800

Fakered 750 mL

$1500/bag

$1500

$1200/bag

$1200

Fakered 1000 mL

$2000/bag

$2000

$1600/bag

$1600

*Assume used one bag of each strength.

Pharmacoeconomic Analysis: • Problem Definition—The objective of this analysis is to determine which artificial blood prod-

uct should be included on the St. AMC drug formulary. • Perspective—This will be from the perspective of the institution.

APPENDIX 132

1155

• Specific treatment alternatives and outcomes—There are two drugs to be compared: artiblood

and fakered. It will be assumed that natural blood products are not an alternative, since the ability to use natural products would preclude consideration of the artificial products. The outcomes to be measured are hospital costs. • Pharmacoeconomic model—A cost-benefit analysis will be performed. A cost-utility analysis would be desirable, but insufficient information is available. Note: No published pharmacoeconomic analysis is available. The following is based on information obtained from the literature concerning efficacy, adverse effects, monitoring, etc., and uses St. AMC costs, since outside prices would be irrelevant. Cost per Patient

Benefit-to-Cost Ratio

Net Benefit

Cost of artiblood (including administration, monitoring, adverse reactions, etc.)

$5120

$7430/$5120 = 1.45:1

$7430 − $5120 = $2310

Benefits of artiblood (money save by early patient discharge from ICU)

$7430

Cost of fakered (including administration, monitoring, adverse reactions, etc.)

$4000

$4500/$4000 = 1.125:1

$4500 − $4000 = $500

Benefits of fakered (money saved by early patient discharge from ICU)

$4500

[Note to reader: The above information is a summary of information, including averages, decision analysis, and sensitivity analysis that would be used in a pharmacoeconomic evaluation. While the details could be presented here, that may be distracting and confusing to some readers—a decision must be made as to whether all of the details will be presented. See Chapter 6 for details on how to prepare a pharmacoeconomic analysis of a drug being evaluated by the P&T committee.]

Presented by John Q. Doe, PharmD, to the Pharmacy and Therapeutics committee on February 30, 20XX.

REFERENCES References would be listed in the order in which they are cited in the text—see Appendix 9–3 in Chapter 9 for format and details.

14–1 Appendix 14–1

Tools Used in Quality Assurance FLOW CHARTS Flow charts illustrate the steps of a process and how the steps are related to each other. It can be used to describe the process, increase a team’s knowledge of the entire process, identify weaknesses or breakdown points in the current process, or design a new process. An example of a flow chart outlining how adverse drug reactions might be addressed within an organization is provided below.

1156

APPENDIX 141

1157

Flowchart: suspected adverse drug reactions Medication administered to patient

No

Was the medication administered as prescribed? Yes

Medication error Was the patient’s response to the medication as expected? Yes

No Is an adverse drug reaction suspected?

Continue to monitor response

No

Reevaluate therapy based on drug information and related patient infomation

Yes

Complete suspected adverse drug reaction reporting form and submit to pharmacy/drug information service; notify physician of event

Report is analyzed (causality, probability, etc.) and follow-up performed as appropriate

Report reviewed by medication use subcommittee

Document event in medical record

Yes

Is it appropriate to modify or update documentation in the medical record?

Cumulative data used (along with data from additional sources) to identify opportunities for improvement

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

PARETO CHART Pareto charts are vertical bar graphs with the data presented so that the bars are arranged from left to right on the horizontal axis in their order of decreasing frequency. This arrangement helps identify which problems to address in what order. By addressing the data represented in the tallest bars (e.g., the most frequently occurring problems or contributing factors) efforts can be focused on areas where the most gain can be realized. Pareto charts are commonly used to identify issues to address, delineate potential causes of a problem, and monitor improvements in processes. An example of a Pareto chart is provided below. This example illustrates frequently occurring factors contributing to improper dose medication errors. By focusing on transcription errors as a contributing factor on which to focus quality improvement efforts, the quality improvement team will generally gain more than by tackling the smaller bars.

35

Transcription errors Duplicate dose

30 25

Illegible handwriting Errant decimal point Wrong schedule

20 15 10 5 0

All others

APPENDIX 141

1159

FISHBONE OR CAUSE-AND-EFFECT DIAGRAM Fishbone or cause-and-effect diagrams represent the relationship between an outcome (represented at the head of the fish) and the possible causes of the outcome (represented as the bones of the fish). The bones of the fish should represent causes and not symptoms of the issue. Fishbone diagrams are commonly used to identify components of a process to address, delineate potential causes of a problem, or identify practitioner groups that participate in producing an outcome and should be represented in the group addressing quality issues in the process(es). An example of a Fishbone chart is provided below.

Equipment

People

Outcomes

Materials

Methods

Causes

Effect

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

CONTROL CHARTS Control charts are run charts or line graphs with defined allowable limits of variation. Data are plotted on the graph as they become available with new data points connected to older data by a continuous line. The x-axis is usually a measure of time. The control limits help identify which variations in data are important. Control limits are statistically determined based on average ranges and sample size. Fluctuation in data points above and below the average is expected and is referred to as common variation or common cause as long as they remain between the control limits. Data points above the upper control limit or below the lower control limit are referred to as special variation or special cause. Special cause variation indicates that something different is going on outside the normal operation of the process. Also, a series of data points above or below average may indicate a trend in performance that may need to be addressed. As variability in a process is reduced by quality improvement efforts, control limits should be recalculated (and narrowed) based on ongoing data. An example of a control chart is provided below. Calls from pharmacists to prescribers in response to questions or issues related to new medication orders are represented over a 6-month period. Data from the month of July indicates a significant increase in the number of calls made. A quality improvement team evaluating this data would then attempt to identify what contributed to this increase. A potential cause in many institutions might be the influx of new medical housestaff into the organization each July. One potential intervention to reduce this special cause is to improve the orientation of new practitioners to the medication use process within the organization.

120 Special variation 100 80 60

Upper control limit Average Common variation

40 20

Lower control limit

0 Apr

May

Jun

Jul

Aug

Sep

14–2 Appendix 14–2

Example of Criteria and Request for Approval

MEDICATION USE EVALUATION CRITERIA Antiemetic Use in the Prophylaxis of Chemotherapy-Induced Nausea and Vomiting Request for Approval by Medication Use Evaluation Committee Purpose of evaluation: The purpose of this Medication Use Evaluation (MUE) is to evaluate the use of antiemetic therapy in the prevention of chemotherapy-induced nausea and vomiting. This agent was selected for evaluation based on its essential role in the management of this patient population, potential inappropriate use, and increased cost relative to other antiemetic agents. This class of medications has not been evaluated within the organization for at least 5 years. Criteria: A multidisciplinary group including physicians, clinical nurse specialists, staff nurses from the oncology unit, and pharmacists developed the attached criteria. They are submitted for approval by the MUE Committee. Data Collection: Data will be collected on all patients with orders for this agent written throughout a period of approximately 30 days beginning in mid-January 20XX. A minimum of 50 cases will be reviewed. Pharmacists and clinical nurse specialists will collect data concurrently from the medical record. Patients will be identified by means of the clinical information system. Results: Results will be presented to this Committee. Information will also be shared with the Cancer Care Committee and Health system Performance Improvement Council. Prescriberspecific results will be confidentially provided to Medical Staff Support for use in the reappointment/recredentialing process.

1161

14–3 Appendix 14–3

Example of MUE Results MEDICATION USE EVALUATION Summary of Overall Results Antiemetic: January-February 20XX Background: This topic was selected based on high use, potential misuse, and high cost of these agents. Criteria for this evaluation were approved at the MUE Committee’s December 20XX meeting. Please refer to attached criteria for additional information. Total Patients Evaluated (All Indications for Use) = 52 Element

Standard

Results

95%

OVERALL RESULTS

Compliance

Prescribing Indication for use

Treatment/prevention of nausea/ vomiting (N/F) associated with chemotherapy

100% (52/52)

Highly emetogenic chemotherapy

46/46

Anticipatory N/V associated with chemotherapy

6/6

Dispensing/Administering Dosing

95%

OVERALL RESULTS

71% (37/52)

Highly emetogenic chemotherapy

31/46

Anticipatory N/V associated with chemotherapy

6/6

Monitoring Adverse drug reaction(s)

< 10-25% (varies with ADR)

OVERALL RESULTS:

4% (2/52)

Headache: 1 patient Constipation: 1 patient continued

1162

APPENDIX 143

Element

1163

Standard

Results

Compliance

95%

OVERALL RESULTS

92% (46/50)*

Outcome Prevention of nausea and emesis

Chemotherapy course not interrupted

95%

Highly emetogenic chemotherapy

41/44

Anticipatory N/V associated with chemotherapy

5/6

OVERALL RESULTS (ALL INDICATIONS)

100% (52/52)

*Includes only patients in whom outcome was documented. Outcome was not assessed in two patients who were discharged immediately following administration of chemotherapy.

Summary of Results Prescribing: Criteria for indication for use were met in all cases. Dispensing/Administering: Criteria for dosing was met in 37 of 52 cases with all cases involving anticipatory nausea and vomiting meeting criteria. In 15 cases, patients receiving the antiemetic prior to highly emetogenic chemotherapy received doses not included in the approved criteria. Five of these patients received doses based on an investigational protocol. This dose is now under consideration by the FDA for approval and preliminary results (available only in abstract form) were recently presented at the American Society of Clinical Oncology meeting. Results with the new dosing regimen have been comparable to those with the currently approved doses. In seven cases not meeting dosing criteria, patients received a single dose prior to chemotherapy consistent with the criteria. However, an additional dose was administered 24 hours after the first dose. These orders were written by two prescribers. Two cases did not meet dosing criteria because the dose was not adjusted based on renal dysfunction. In both cases, the estimated creatinine clearance was between 20 and 25 mL/min and nephrotoxic drugs were not being administered concurrently. In both cases, the estimated creatinine clearance increased to 30 mL/min or more by day two of the admission (probably due to rehydration of the patient). Neither patient experienced adverse effects. One dose was not administered within the appropriate timeframe. In this case, the antiemetic dose was administered just 5 minutes prior to the initiation of chemotherapy administration. The nurse administering the antiemetic documented its administration on the way to the patient’s room. When she arrived, the patient was not in the room. The dose was administered after he was located, approximately 25 minutes later. The nurse did not correct the actual administration time until after the chemotherapy was administered by a second nurse. Recommendations: 1. Add new dosing regimen to dosing criteria. 2. Send letters to prescribers giving extra dose. 3. Renal dosing was not significantly outside guidelines. Mention findings in the report to be published in the quality improvement newsletter but do not take prescriber-specific action.

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

4. The dose administered late was reported via an incident report, no further action by this group is required at this time. Monitoring: The rate of adverse drug reactions was less than that reported in the literature. This might be reflective of underreporting and underdocumenting of adverse drug events. Recommendations: 1. The Adverse Drug Event Task Force is currently implementing a new process to improve reporting and documentation. No specific action by this group is required at this time. Outcome: Ninety-two percent of patients did not experience nausea or vomiting. Outcome was assessable in 50 patients; two patients were discharged immediately following the administration of chemotherapy. The patient who received his antiemetic dose just 5 minutes prior to chemotherapy experienced moderate nausea and no vomiting. Otherwise, the occurrence of nausea and vomiting was not related to problems with administration or dosing. Recommendations: 1. 92% success rate is acceptable based on literature, no action is necessary. General Recommendations: 1. After approval, implement recommendations presented above. 2. Publish results in the Quality Improvement Newsletter following review by the Cancer Care Committee and the Quality Improvement Committee. 3. Perform a follow-up evaluation focusing on dosing issues. 4. Initiate planned assessment of this agent’s use in postoperative nausea and vomiting as soon as possible.

14–4 Appendix 14–4

Evaluation Form for Drug Information Response Request #

Date of Request DI Staff

Response by (circle one): Caller (circle type):

MD

RPh

Resident Nurse

Student Other:

Assessment of Search and Response to Request Yes

No

NA

Standard %*

1. Is requestor’s demographic information complete?

100%

2. Background information is:

100%

A. Thorough B. Appropriate to request 3. Is the question clearly stated?

100%

4. Search strategy/references:

100%

A. Appropriate references were used B. Search was sufficiently comprehensive C. Is search strategy clearly documented 5. Response was:

100%

A. Appropriate for the situation B. Sufficient to answer the question C. Provided in a timely manner D. Integrated with available patient data E. Supported by appropriate materials supplied to requestor 6. If complete response could not be provided within timeframe requested, was requestor advised as to the status of their request and the anticipated delivery of the final response?

100%

* If performance falls below 90% in any category during any month, the service director will coordinate an assessment of the process and report findings and actions will be reported to the P&T committee.

Comments:

Reviewed By:

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15–1 Appendix 15–1

Kramer Questionnaire*

Start, Axis I

Is the CM* Widely Known and Universally Accepted as an Adverse Reaction to the Suspected Drug?

1

Yes

2

Is the CM Known to Occur at the Dosage Received in This Case?

No or DK† Consult a Recent Edition of the Physicians’ Desk Reference or American Hospital Formulary Service.‡ Is The CM Listed as an Adverse Reaction to the Suspected Drug in the Dosage Received?

3

Yes

No or DK

Yes Score +1

No Has Enough Clinical Experience Accumulated With the Drug So That Most Adverse Reactions to it Are Likely to Have Been Previously Reported?

4

No or DK‡

Score 0

Yes Score −1

Go to “Start, Axis II”

*Abbreviation CM indicates clinical manifestation, the abnormal sign, symptom, or laboratory test, or cluster of abnormal signs, symptoms, and tests, that is being considered as a possible adverse drug reaction. † Abbreviation DK indicates do not know. This answer should be given when no data are available for the question being answered or when the quality of the data does not allow a firm “Yes” or “No” response. ‡ When these are not available, an equivalent reference source may be used.

Figure 1. Axis I. Previous general experience with drug.

∗

Kramer MS, Leventhal JM, Hutchinson TA, Feinstein AR. An algorithm for the operational assessment of adverse drug reaction: I. background, descriptions, and instructions for use. JAMA. 1979; 242(7):623-32.

1166

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APPENDIX 151

Start, Axis II

No

6

Is the Preexisting Condition Commonly Followed by This Type of Change? DK

Is the CM a Change (Exacerbation, Recurrence, Complication, or New Manifestation) in a Preexisting Clinical Condition, i.e., a Condition Present Before the Administration of the Suspected Drug?

5

Yes

Yes

No or DK Is the CM Consistent in Quality and Severity With Any New Alternative Etiologic Candidates* Other Than a Preexisting Condition?

9 Score – 1 and Go to “Start, Axis III”

No

DK

10

Was the CM Consistent in Timing With Any of These Alternative Candidates?

Score + 2 No 13

Yes Score +1

No

11

Are There Any New Alternative Candidates* That Could Explain This Change?

Was a Score of +1 Obtained on Axis I?

No

Yes

8

No

Yes or DK

Yes 7

Does the CM Commonly Occur in This Type of Patient In the Absence of Recognizable Etiologic Candidates?

Yes

DK Score 0 and Go to “Start, Axis III”

Are There Any New Alternative Candidates* That Could Explain This Change?

12

Is the CM Commonly Seen With Any of These Alternative Candidates?

No

Yes

Yes

No Score +1

Score – 1

Score 0 Go to “Start, Axis III”

Figure 2. Axis II. Alternative etiologic candidates. For explanation of abbreviations, see Axis I. Start, Axis III

14 Is the Timing of the Appearance of the CM Relative to Administration of the Suspected Drug Difficult or Impossible to Assess Because the CM Represents an Equivocal Change in a Preexisting Clinical Condition?

Yes

Score 0

No 15 Is the Drug-CM Association So Unusual as to Prevent Knowing What Timing to Expect for an Adverse Drug Reaction of This Type?

Yes

Score 0

No 16 Was Timing Inconsistent With an Adverse Drug Reaction to This Drug?

Yes

Score – 2

No or DK 17 Given the Type of CM, Was the Timing Not Only Consistent With, but as Expected for an Adverse Drug Reaction to This Drug?

No Score 0 or DK

Yes Score +1

Figure 3. Axis III. Timing of events. For explanation of abbreviations, see Axis I.

Go to “Start, Axis IV”

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

Start, Axis IV

No or DK

18 Is the CM a Pharmacologic, ie, Dose-Related Type of Manifestation? Yes 19 Is the Result Available for Serum, Urine, or Other Body Fluid Level of the Drug, or a Metabolite of the Drug? Yes

No

21 Taking Its Timing Into Consideration, Does This Level Definitely Support the Diagnosis of Overdose for This Patient?

No

Yes

Yes DK

20 Is There Unequivocal Evidence That the Amount of Drug Received Was an Overdose for This Patient?

Yes

Score +1

Score 0

No No

22 Is the Level Strongly Against the Diagnosis of Overdose for This Patient?

Yes

23 Is This CM Likely to Represent an Idiosyncratic Overreaction of This Patient to the Drug?

Yes

Score 0

No Score – 1 Go to “Start, Axis V-A”

Figure 4. Axis IV. Drug levels and evidence of overdose. For explanation of abbreviations, see Axis I.

Start, Axis V-A

24 Is Dechallenge Difficult or Impossible to Assess Because of Any of the Following: (a) Death Caused by, or Secondarily Consequent to, the CM, (b) an Irreversible CM, or (c) a CM Whose Resolution Would Not Usually Be Altered by Removal of the Causative Agent?

No or DK

Yes

Yes

25 Is the Total Score on Axes I-IV +3?

Score +1

No Score 0

26 Is the CM Characteristically Transient and Episodic?

27 Was a Pattern of Episodes Established While the Patient Was Taking the Drug?

Yes

28 Was the Drug Discontinued After the CM Appeared?

No

Yes

No or DK

Yes

29 Did the CM Recur After Discontinuation?

No

Yes

Score – 1

No or DK Score 0

30 Is the CM a Pharmacologic, ie, Dose-Related, Type of Manifestation?

Yes

31

or DK

No

DK Score 0 Go to “Start, Axis V-B” 37 Was the Period of Observation Long Enough to Assess Dechallenge Adequately? Yes Score – 1

No

Yes

No

35 Was the Drug Discontinued While the CM Was Present (or While a Pattern of Episodes Was Occurring)? No

Was the Dosage Substantially Reduced Without or Before Being Discontinued?

Yes

36 Did the CM Diminish or Disappear at Any Time After Dicontinuation of the Drug?

No or DK Go to “Start, Axis V-C”

Yes

Yes

32 Was the Dosage Reduced While the CM Was Present (or While a Pattern of Episodes Was Occurring)? Score 0

Yes

No 33 Did the CM Substantially Diminish or Disappear After Dosage Reduction but Before Complete Discontinuation?

No

No

34 Was the Drug Subsequently Discontinued? Yes

Score 0

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Go to “Start, Axis IV”

Figure 5A. Axis V-A. Dechallenge: difficult assessments. For explanation of abbreviations, see Axis I.

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

Start Axis V-B

38

Did the CM Substantially Diminish or Disappear While the Patient Was Taking the Drug?

No

Yes 39

Was an Agent or Maneuver Administered That Was Specifically Directed Against the CM and That Usually Produces the Degree and Rate of Improvement Observed in This Case?

Yes

No 40

Is the Improvement in the CM Most Likely Caused by the Development of Tolerance to the Drug and is Tolerance a Well-Described Phenomenon With the Drug?

Yes Score 0

No Score –1

Go to “Start, Axis VI”

Figure 5B. Axis V-B. Dechallenge: absence of dechallenge. For explanation of abbreviation, see Axis I.

APPENDIX 151

1171

Start, Axis V-C

41 Was the CM (or Established Pattern of Episodes) Constant or Progressing at the Time of Dechallenge?

No

Yes 42 Were the Degree and Rate of Diminution or Disappearance of the CM as Expected for an Effect of Drug Withdrawal?

No

Yes

No

43 Was an Agent or Maneuver Administered That Was Specifically Directed Against the CM and That Usually Produces the Degree and Rate of Improvement Observed in This Case? Yes 44 Would This Agent or Maneuver Be Expected to Improve This Type of CM Regardless of Whether or Not It Was Caused by the Suspected Drug?

Yes Score 0

No 45 Was There a Good Alternative Candidate That Resulted in a Score of –1 on Axis II?

No Score +1

Yes 46 Was There an Unequivocal Improvement in or Disappearance of This Alternative Etiologic Candidate That Could Explain the Improvement in the CM?

Yes Score 0

No Score +1

Go to “Start, Axis VI”

Figure 5C. Axis V-C. Dechallenge: improvement after dechallenge. For explanation of abbreviation, see Axis I.

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

Start, Axis VI

No

47 Was the Drug Discontinued and Then Readministered?

No or DK

48 Is the CM a Pharmacologic, i.e., Dose-Related, Type of Manifestation?

or DK

Yes

Yes 49 Was Dosage Substantially Increased After Previous Reduction in Dosage?

No or DK

Yes 50 Was the CM Either Progressing or at Such a Level of Severity That Any Recurrence or Exacerbation Would Be Difficult to Appreciate?

Yes or DK

No

No

52 Have Any New Clinical Conditions or Recent Diagnostic or Therapeutic Interventions Occurred (Including Drugs Begun Since the Appearance of the Original CM) That Could Explain This Recurrence or Exacerbation?

Yes

51 Did the CM Recur or Clearly Exacerbate After Rechallenge? DK

Yes

Score 0

No Score + 1 Score 0 53 Is There Unequivocal Evidence That the Dosage or Duration of Drug Administration on Rechallenge Was Less Than the Dosage and Duration Suspected of Causing Original CM?

55 Did the Patient Receive Another Agent or Maneuver That Would Be Expected to Prevent Recurrence or Exacerbation of the CM?

No

Yes

Yes 54 Is the CM a Pharmacologic, i.e., Dose-Related, Type of Manifestation?

No

Score – 1

No or DK

Yes 56 Was Rechallenge Subsequently Attempted With a Higher Dosage?

No

Stop

Score 0

Yes Outline of Scoring Strategy* +1 †

0

–1 ‡

Axis I

CM well accepted as ADR to suspected drug

CM is not well known or drug is new

CM previously unreported as ADR to well-known drug

Axis II

(a) No good alternative candidate (score +2); or

Candidate(s) exist, but no good ones

Good alternative candidate

(b) Otherwise unexplained exacerbation or recurrence of underlying illness (score +1) Axis III

Timing as expected for ADR for this drug-CM pair

Timing equivocal or nonassessable

Timing inconsistent for ADR for this drug-CM pair (score –2)

Axis IV

Drug level or other data provide unequivocal evidence of overdose

Unobtained, unknown, or equivocal level or other evidence of overdose

Drug level strongly against overdose

Axis V

(a) CM Improves suitably after dechallenge; or

(a) CM improved, but degree or rate are unexpected; or

(a) CM improves without dechallenge; or

(b) Nature of CM prevents assessment of dechallenge for otherwise likely ADR

(b) CM is treated by auxiliary maneuver

(b) Potentially reversible CM fails to improve after dechallenge

CM unequivocally recurs or exacerbates on rechallenge

(a) No rechallenge attempted; or (b) Response of CM obscured by auxiliary maneuver

CM fails to recur or exacerbate on rechallenge

Axis VI

* CM indicates clinical manifestation; ADR = adverse drug reaction. † Except where noted as +2. ‡ Except where noted as –2.

Figure 6. Axis VI. Rechallenge. For explanation of abbreviation, see Axis I.

15–2 Appendix 15–2

Naranjo Algorithm* To assess the adverse drug reaction, please answer the following questionnaire and give the pertinent score. Yes

No

Don’t Know

1. Are there previous conclusive reports on this reaction?

+1

0

0

2. Did the adverse event appear after the suspected drug was administered?

+2

−1

0

3. Did the adverse reaction improve when the drug was discontinued or a specific antagonist was administered?

+1

0

0

4. Did the adverse reaction reappear when the drug was readministered?

+2

−1

0

5. Are there alternative causes (other than the drug) that could on their own have caused the reaction?

−1

+2

0

6. Did the reaction reappear when a placebo was given?

−1

+1

0

7. Was the drug detected in the blood (or other fluids) in concentrations known to be toxic?

+1

0

0

8. Was the reaction more severe when the dose was increased, or less severe when the dose was decreased?

+1

0

0

9. Did the patient have a similar reaction to the same or similar drugs in any previous exposure?

+1

0

0

10. Was the adverse event confirmed by any objective evidence?

+1

0

Score

0 Total Score

Score Interpretation ___Definite: ≥ 9 ___Probable: 5 to 8 ___Possible: 1 to 4 ___Doubtful: ≤ 0 ∗

Naranjo CA, Busto U, Sellers EM, Sandor P, Ruiz I, Roberts EA, et al. A method of estimating the probability of adverse drug reactions. Clin Pharmacol Ther. 1981;30(2):239-45.

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15–3 Appendix 15–3

Jones Algorithm* START HERE:** Does event have a reasonable temporal association with use of the drug?

No

Causal relationship considered remote.

No

Causal relationship considered possible.

No

Causal relationship considered possible.

Yes

Was there a dechallenge from the drug? Yes

Did the observed event abate upon dechallenge? Yes

Was there a rechallenge?

No

Y es

Did the reaction or event reappear upon rechallenge?

Could the event be due to an existing clinical condition?

No

Causal relationship considered probable.

Y es No

Causal relationship considered possible.

Yes

Causal relationship considered highly probable. **Each drug is carried through independently; if 1 drug was dechallenged or rechallenged simultaneously causality for all is possible. QUESTIONS: 1. Did the reaction follow a reasonable temporal sequence? 2. Did the patient improve after stopping the drug? 3. Did the reaction reappear on repeated exposure (rechallenge)? 4. Could the reaction be reasonably explained by the known characteristics of the patient’s clinical state?

*

Jones JK. Adverse drug reactions in the community health setting: approaches to recognizing, counseling, and reporting. Clin Comm Health. 1982;5(2):58-67.

1174

15–4 Appendix 15–4

MedWatch Form

1175

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

APPENDIX 154

1177

17–1 Appendix 17–1

Investigational New Drug Application

1178

APPENDIX 171

1179

17–2 Appendix 17–2

Statement of Investigator

1180

APPENDIX 172

1181

17–3 Appendix 17–3

Protocol Medication Economic Analysis Date: Protocol title: Study chairperson: Hospital Cost Analysis Hospital Cost per Cycle*

Drug

Number of Cycles

Total Cost per Patient

Number of Patients

Total Protocol Cost

Annual Cost

Primary Therapy

Supportive Care

2

*When applicable, doses calculated on 1.7 m or 70 kg at initial dose level and costs include infusion fluids, administration sets, and tubing. Patient Charge Analysis Patient Charge per Cycle*

Drug

Number of Cycles

Total Charge per Patient

Number of Patients

Total Patient Billing

Primary Therapy

Supportive Care

*When applicable, charges include infusion fluids, administration sets, and tubing. Reimbursement Risk Drug

Comments Summary

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FDA Labeled

Compendium

17–4 Appendix 17–4

Investigational Drug Accountability Record Form approved OMB No. 0925-0240 Expires: 6/30/91 PAGE NO. _______________

National Institutes of Health National Cancer Institute

CONTROL RECORD

Investigational Drug Accountability Record

SATELLITE RECORD

Name of Institution

Protocol No. (NCI)

Drug Name, Dose Form and Strength Dispensing Area

Protocol Title Investigatior Line No.

Date

Patient’s Initials

Patient’s I.D. Number

Dose

Quantity Dispensed or Received

Balance Forward Balance

Manufacturer and Lot No.

Recorder’s Initials

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. NIH-2564 9-85

1183

18–1 Appendix 18–1

Policy Example: High-Alert Medications I. PURPOSE: This policy outlines the process for the safe use of high-alert medications.

II. DEFINITIONS: When used in this policy these terms have the following meanings: A. High-alert medications: Medications that have a higher risk of causing harm when an error occurs. B. Independent double verification (IDV): 1. Is performed by two staff members (as appropriate to the task, e.g., blood administration, breast milk retrieval) in the same proximity but separately, without prompting by another, as an independent cognitive task. 2. Both professionals will dialog to confirm what was checked independently prior to administration. 3. IDV of a medication also includes the following steps in addition to the two steps above: a. Performed by two professionals (e.g., nurse/pharmacist,/physician [within the approved LPN scope of practice]) in the same proximity but separately, without prompting by another, as an independent cognitive task. b. Each professional must check: the actual prescriber’s order, drug, calculation, concentration, and other information specific to the medication. c. Each professional performing the verification performs all calculations independently without knowledge of any prior calculations and documents the verification in the medical record.

III. POLICY: It is the policy of the health system that: A. All systems and data repositories relating to medication use (medication error reports, adverse drug event reports, etc.) shall be systematically evaluated on an ongoing basis to identify those medications in the hospital formulary determined to be high-alert medications. B. Medications being added to the hospital formulary shall be evaluated for their high-alert potential.

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APPENDIX 181

1185

C. Medications identified as high-alert shall be targeted for specific error reduction interventions. D. The following three principles shall be followed to safeguard the use of high-alert medications: 1. Reduce or eliminate the possibility of error (e.g., limit the number of high-alert medications on the hospital formulary; remove high-alert medications from the clinical areas). 2. Make errors visible by detecting serious events before they reach the patient (e.g., follow the five rights and when appropriate utilize the independent double verification process). 3. Minimize the consequences of errors (e.g., stock high-alert medications in smaller volume units of use minimizing the error effect if the medication was administered in error). E. Engineering safety controls shall be used as appropriate.

IV. PROCEDURE: A. The Pharmacy and Therapeutics Committee will approve all medications added to the formulary. B. The following processes for safeguarding high-alert medication use have been implemented: 1. Build in system redundancies (e.g., unit dose drug distribution). 2. Use fail-safes (e.g., pumps with locking mechanisms). 3. Reduce options (e.g., limit concentration available). 4. Utilize engineering safety controls (e.g., oral syringes that will not fit IV tubing, computer systems that force the order of standardized products). 5. Externalize or centralize error-prone processes (e.g., centralize IV solution preparations). 6. Use differentiation (e.g., identify and isolate look-alike and sound-alike products, use generic names). 7. Store medications appropriately (e.g., separate potentially dangerous drugs with similar names or similar packaging). 8. Screen new products (e.g., inspect all new drugs and drug delivery devices for poor labeling and/or packaging). 9. Standardize and simplify order communication (e.g., only approved abbreviations will be used, all verbal orders will be read back verbatim to the ordering physician). 10. Limit access (e.g., high-alert medications will be securely stored). 11. Use of constraints (e.g., pharmacy will screen all medication orders, automatic stop orders or duration limits). 12. Standardize or automate dosing procedures (e.g., use of standard dosing charts rather than calculating doses based on weight or renal function when appropriate). C. When initiating any high-alert medication by any route, and for those high-alert medications administered via pump, and with all subsequent bag/syringe changes and with dose changes requiring pump adjustment, the following must occur: 1. Independent double verification, each professional will independently: a. Review/verify the physician order in the medical record (i.e., on the physician’s order sheet or in the clinical information system).

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

b. Verify the correct medication, dose (all required dosage calculations must be done independently by each nurse), frequency/rate/titration, and route against the order. 1) Note: After the initial IDV, ongoing titration in Level I areas is exempt from the IDV process for each continuing adjustment. 2) Some medications require IDV of pump settings at change of shift (e.g., insulin). 3) Additional information regarding medication-specific IDV requirements is available in the IDV Policy and Procedure. 2. The nurse administering the medication will: a. Identify the patient using two acceptable identifiers. b. Verify the correct connection/patient access when administering the medication via an intravenous drip, trace the flow of the medication from the bag > to the pump > to the patient access, prior to hanging the medication. c. Document the verification. D. When an infusion device with prebuilt infusion parameters is used and pre-set infusion parameters are not available for a high-alert medication (e.g., when the medication is new and not yet added to the library), independent double verification of the medication parameters programmed into the pump must occur prior to initiation of the infusion.

V. DOCUMENTATION: As appropriate in the medical record or clinical information system.

VI. REFERENCES: A. B. C. D. E. F. G. H. I. J. K. L. M.

Institute for Safe Medication Practices (ISMP); ISMP’s list of high-alert medications. 2008. Patient Care Policy and Procedure #0282, Independent Double Verification. Patient Care Policy and Procedure #0500, Verbal Orders. Patient Care Policy and Procedure #0700, Abbreviations. Patient Care Policy and Procedure #0282, IDV. Patient Care Policy and Procedure #5010, Anticoagulant Safety. Patient Care Policy and Procedure #5020, Chemotherapy: Oncology/Hematology. Patient Care Policy and Procedure #5070, Electrolyte Infusions. Patient Care Policy and Procedure #5117, Insulin Intravenous Infusions. Patient Care Policy and Procedure #5130, Medication Administration. Patient Care Policy and Procedure #5131, Medfusion Syringe Infusion Pump. Patient Care Policy and Procedure #5140, Medication Use Analysis/Error Prevention. Patient Care Policy and Procedure #5150, Moderate/Deep Sedation/Analgesia.

VII. ATTACHMENTS: High-Alert Medications/Classes, one page. Source: Used by permission from Orlando Health, Orlando, FL. www.orlandohealth.com.

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APPENDIX 181

High-Alert Medications/Classes

Safeguards, Etc.

Chemotherapy agents (all routes, includes antineoplastic, biological, immunological agents used for malignant oncology and hematology diagnoses)

Chemotherapy Policy and Procedure outlines requirements for independent double verification(IDV) of order, laboratory parameters, body surface area, medication, dose, route, frequency, etc. Chemotherapy order form (or electronic equivalent) required Only attending physicians with chemotherapy privileges may write orders No verbal or telephone orders allowed (except to clarify as outlined in policy) Only Chemo-verified RN’s may administer (exceptions are made for some oral agents, refer to the Chemotherapy Policy and Procedure for details)

IV Electrolytes (i.e., potassium chloride and phosphate, concentrated sodium chloride, magnesium sulfate, calcium chloride, and calcium gluconate)

Independent double verification required (IDV not required for 1000 mL pre-mixed IV solutions) No concentrated products outside Pharmacy (Rare exceptions exist, but only with specific safeguards) Electrolyte policy provides specific administration parameters and limits Electrolyte replacement protocol includes dosing and monitoring parameters Standard concentrations/premixes

Intravenous/subcutaneous anticoagulants (i.e., heparin, lepirudin, enoxaparin, argatroban, bivalirudin—excluding flushes)

Independent double verification required Weight-based protocol for heparin. The prescriber must designate the specific protocol (e.g., Cardiac, Noncardiac) to be implemented Duplication warning in Clinical Information System Standardized order review requirements for enoxaparin and fondaparinux Standardized laboratory assessments for heparin, enoxaparin, and fondaparinux for treatment of deep vein thrombosis/pulmonary embolism Premixed heparin solutions in standard concentrations

Neuromuscular blocking agents

IDV required Availability limited to specific units and access limited on these units (e.g., emergency department, operating room, intensive care unit) Special labeling of packages Standard concentrations established

Insulin

IDV required (Note: verification of the insulin product is not required when insulin is supplied directly from pharmacy in patient-specific units of use (e.g., prefilled syringes versus vials) Floor stock limited to specific agents and Pharmacy removes unused patient-specific vials from patient care units daily Resources include the Insulin Infusion Policy and the IV push insulin parameters defined within policy Sliding scale order sets create a consistent process continued

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

High-Alert Medications/Classes

Safeguards, Etc.

Anesthetic agents used outside the OR (e.g., propofol, ketamine, methohexital, etomidate, dexmedetomidine)

IDV required

Warfarin

IDV required

Guidelines for use and administration of propofol These agents have been added to the Moderate-Deep Sedation Policy with defined safeguards for use Standard administration time to allow access to International Normalized Ratio (INR) results prior to daily dosing Laboratory monitoring standards and standardized order review processes Critical value—clinical laboratory calls with INR results >5 Automated dispensing cabinet (ADC) inquiry—nurse is asked if he/she knows the patient’s current INR as warfarin is being taken from ADC for administration to the patient Pharmacy monitoring of at-risk patients

18–2 Appendix 18–2

Policy Example: Medication Shortages and Backorders I. PURPOSE: The purpose of this policy is to outline an appropriate response to current or impending medication shortages in order to provide patients appropriate alternative therapy. In addition, the policy will ensure proper communications among the health system facilities to develop a unified action plan in addressing medication shortages.

II. DEFINITIONS: When used in this policy these terms have the following meanings: A. Shortage: A drug product shortage is a supply issue that affects how the pharmacy prepares or dispenses a drug product or influences patient care when prescribers must use an alternative agent. B. Backorder: A short-term and/or long-term unavailability of drug products.

III. POLICY: A. It is the policy of the health system that medication shortages and/or backorders shall be handled in an efficient, consistent, and timely manner. B. Pharmacy Services shall gather information and work collaboratively to assess alternatives, develop communication plans, and ensure safety when handling medication shortages or backorders.

IV. PROCEDURE: A. The individual aware of the shortage or backorder will notify the Corporate Pharmacy Contracting Coordinator as soon as it has been identified. B. On identification of a medication shortage the Corporate Pharmacy Contracting Coordinator or designee will activate the communications with the pharmacy buyers to obtain an accurate inventory count and site-based utilization data. C. The pharmacy buyers will also proceed with the following steps:

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DRUG INFORMATION: A GUIDE FOR PHARMACISTS

D.

E.

F.

G.

1. Sending a completed Medication Shortage Form (Attachment A) to the site-based Pharmacy Management Team and the Corporate Pharmacy Contracting Coordinator. The entire form must be completed to the extent possible. Any information not readily available may be left blank. 2. Conduct an accurate and complete inventory inclusive of medication supply in satellite pharmacies, automated dispensing cabinets, storage, main pharmacy, code carts, medication kits, and other procedural areas. 3. During severe shortages (less than 2-week supply) the decentralized medication stock (ADM, satellite pharmacy, etc.) will be quarantined to the main pharmacy with the approval of the Pharmacy Manager or designee. The Pharmacy Manager or designee must communicate this decision with the pharmacy staff. 4. Maximize purchasing according to allocation at each site. 5. Transfer and balance the utilization of the remaining supply with facilities in greatest need and inform site-based Pharmacy Management Team. For severe and acute shortages a group (Backorder Action Team) will convene under the direction of the Corporate Pharmacy Contracting Coordinator. The group will be responsible for developing a detailed backorder/shortage action plan for alternatives, communications, and a monitoring of the backorder for updates. The group may be inclusive of, but not limited to, the Drug Information Coordinator, Pharmacy Managers, Operations Coordinators, Clinical Coordinators, and when applicable Clinical Specialist, Pyxis® System Specialist, Pharmacy Information Systems Coordinator, Pharmacy Buyer, Pharmacy Educator, and Risk Management. The Drug Information Coordinator will assist with developing a list of alternatives and a plan for substitution in the event the medication supply becomes depleted. This plan must be forwarded to the site-based Pharmacy Manager or designee. Therapeutic substitutions must receive the approval of the Pharmacy and Therapeutics Committee Chair or designee prior to implementation. The organizational backorder/shortage action plan should include the following information: current supply and utilization, how long will current supplies last, anticipated duration of the shortage/backorder, plan to conserve current supply (assessment of medical necessity [patient prioritization]), development of therapeutic alternatives, and dosing when all supplies are depleted. With a clear operational assessment of impact and a clinical impact on patient care, the group will develop a communication plan when appropriate (for pharmacists, nurses, physicians, respiratory therapists, or other health care professionals as needed). The Pharmacy Manager or designee will implement the communication plan for each site, respectively. 1. Plans to address a corporate shortage will include notification of at least the following groups and individuals: a. Chief Nursing Officer, Clinical Unit Educators, and Nurse Managers b. Chief of Staff, Chief Medical Officers, and Medical Department Chairpersons. Notices to the Clinical Information System and faxes can be facilitated via Medical Staff Support and Information Services.

APPENDIX 182

1191

c. Managers of other departments impacted by the shortage/backorder (such as the clinical laboratory, respiratory therapy, radiology, infection control, and others). d. Information Services should be notified if clinical information systems are impacted (e.g., if an alternative medication must be added to the system, if an interchange must be implemented, etc.) 2. Committees with oversight for the clinical area should also be notified as appropriate, such as the Corporate Code Blue Committee, Critical Care Committee, Surgical Issues, and others. 3. Plans may be site specific as appropriate.

V. DOCUMENTATION: None.

VI. REFERENCE: ASHP Guidelines on Managing Drug Product Shortages. Am J Health-Syst Pharm. 2009;66:13991406.

VII. ATTACHMENTS: A. Medication Shortage/Backorder Form, one page B. Decision Flow Diagram Source: Used by permission from Orlando Health, Orlando, FL. www.orlandohealth.com.

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Medication Shortage/Backorder Form Pharmacy: Drug Name: Generic: Brand: Drug formulation(s) and/or strength(s) affected:

Therapeutic interchanges affected: Backorder or Discontinuation. If backorder, anticipated duration/resupply date: Current Supply: Current Usage: How much additional supply can be obtained: Cost concerns related to obtaining product from outside sources: Reason for shortage/backorder (if known): Raw and bulk material unavailability Manufacturing difficulties Voluntary recalls Manufacturer production decisions (e.g., discontinuation of product) Orphan drug products Restricted drug distribution Industry consolidations Market shifts Unexpected increases in demand Nontraditional distributors (e.g., international) Natural disasters Other: Other comments: Threat to Patient Care and Cost Assessments: Assessment by Pharmacy Contracting Coordinator: Alternative sources (other wholesalers, direct purchases, etc.): Assessment by Drug Information Coordinator: Alternative therapies: Medical necessity:

APPENDIX 182

Identification and monitoring of Medication backorder or shortage Responsible party: all

Notification to the corporate pharmacy contracting coordinator

Corporate Contracting Coordinator will inform ALL Pharmacy Buyers

Pharmacy Buyers will proceed with following steps once notified: • Conduct an accurate and complete inventory (ADM, main pharmacy, satellite, code carts etc.) • Complete Medication Shortage Form (Attachment A) and send to Pharmacy Management Team and Corporate Contracting Coordinator • Quarantine decentralized supply when applicable for severe shortages (with approval from Pharmacy Manager or designee) • Maximize purchasing according to allocation Transfer supplies to sites in greatest needs (inform Pharmacy Manager)

The health system is experiencing severe or acute shortage

Corporate Contracting Coordinator: • Pursue alternative sources for securing additional supply Corporate Pharmacy Contracting Coordinator or Site-based Pharmacy Manager: • Convenes a call or a meeting with the Backorder Action Team to develop a backorder/shortage action plan • Backorder Action Team to develop a communication plan for the health care team as appropriate • Pharmacy Manager or designee must implement plan at their individual sites • Plan must be revised and redistributed as the situation evolves Drug Information Coordinator • Develop a list of alternatives and a plan for therapeutic substitution when necessary • Receive endorsement/approval from Pharmacotherapy Committee Chair or designee • Pharmacy Manager or designee must implement plan at their individual sites

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22–1 Appendix 22–1

Response Letter Drug A—Incidence of Yellow Stripes DRUG A—Adverse Event—Yellow Stripes Dear Dr. Smith, Thank you for your inquiry. The following information is provided in response to your question regarding the use of DRUG A® and the incidence of yellow stripes appearing on the skin. Please note that the information provided is not intended to advocate the use of our product in any manner other than as described in the enclosed full prescribing information.

INDICATIONS DRUG A® is indicated for the relief of moderate to severe hiccups in patients 18 years of age or older.1

PRESCRIBING INFORMATION Please refer to the following sections of the enclosed Full Prescribing Information that are relevant to your inquiry: ADVERSE REACTIONS, WARNINGS, PRECAUTIONS.1

LITERATURE SEARCH RESULTS A literature search of MEDLINE databases (and other resources) pertaining to the incidence of yellow stripes appearing on the skin associated with the use of DRUG A® was conducted through May 2010.

CLINICAL STUDIES In phase III efficacy and safety studies of DRUG A® for the relief of moderate to severe hiccups, yellow stripes appearing on patients skin was reported in 7% of patients (see Table: Incidence of Yellow Stripes Appearing on Skin).2

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TABLE 1 INCIDENCE OF YELLOW STRIPES APPEARING ON SKIN2 Adverse Event

DRUG A®

Placebo

Yellow stripes on skin

7%

7%

In a 12-month long-term safety study, the incidence of yellow stripes appearing on the skin was evaluated. DRUG A® users reported yellow stripes on their skin with an incidence rate of 9% (see Table: Incidence of Yellow Stripes Appearing on Skin—Long-Term Study).3 TABLE 2 INCIDENCE OF YELLOW STRIPES APPEARING ON SKINLONGTERM STUDY3 Adverse Event

DRUG A®

Placebo

Yellow stripes on skin

9%

8%

Multiple case reports were identified reporting yellow stripes appearing on patient’s skin. In a report by Jones and associates,4 a 90-year-old woman suffering from severe hiccups reporting yellow stripes appearing on the skin within 4 days of starting treatment. She was treated with DRUG FIX-IT® and her yellow stripes resolved immediately. In a report by Jones and associates,5 a 20-year-old man suffering from moderate hiccups reported yellow stripes on his skin within 2 minutes of starting treatment. The patient did not seek treatment, continued taking DRUG A®, and the yellow stripes resolved.

ADVERSE EVENT REPORTING Please see the enclosed Prescribing Information for the complete safety and drug interaction information on our product. In order to monitor the safety of our products, we encourage clinicians to report adverse events by calling 1-800-555-9999 from 9 am to 9 pm Mountain Time, Monday through Sunday. Adverse events may also be reported to the FDA MedWatch program by phone (1-800-FDA-1088), fax (1-800-FDA-0178), or e-mail (www.fda.gov/medwatch). To view a description of ongoing clinical trials for our products, please visit www.clinicaltrials.gov.

REFERENCES 1. DRUG A® [package insert]. Awesome Drugs R Us, Inc. Any Town, USA; 2010. 2. Jones Z, et al. Efficacy and safety of DRUG A® in the treatment of hiccups. JAHA. 2009;6542: 735-746. 3. Jones Y, et al. Long-term safety of the use of DRUG A® in severe hiccups. JAHA. 2009; 6543: 887-896. 4. Jones A, et al. 90-year old woman with yellow stripes. Hiccup Central. 2009;78(4):232-235. 5. Jones B, et al. 20-year old male with resolved yellow stripes. Hiccup Central. 2010;83(3):196-199.

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Glossary A priori In clinical trial design, this distinguishes something that is done before the study is started. Determining specific study criteria prior to study initiation. Absolute risk reduction The difference in the percentage of subjects developing the adverse event in the control group versus subjects in the intervention group. Also refers to the number of subjects spared the adverse event by taking the intervention compared to the control. Abstracting service A database that provides abstracts and citations for journal articles. Abstracts A synopsis (usually of 250 words or less) of the most important aspect(s) of an article. Academia Pertaining to a college, school, or other educational institution. Academic detailing A process by which a health care educator visits a physician to provide a 15- to 20-minute educational intervention on a specific topic. Information provided is based on the physician’s prescribing patterns and evidence-based medicine to improve prescribing. Accountable care organization A collaborative group of hospitals, doctors, and other providers of health care who coordinate their patient care efforts for Medicare patients. An emphasis is placed on minimizing duplication of effort and preventing medical errors, in particular, for the chronically ill. Action-guides A term coined by Beauchamp and Childress to refer to a hierarchical approach to analysis of an ethical issue when forming particular judgments about the issue. Active control A standard therapy or procedure (but not a placebo) used in a study to determine the difference in effect produced by the study intervention. Adaptive clinical trial A trial design, also known as group sequential design, that allows adaptation of various components such as inclusion/exclusion criteria, dosing, efficacy outcomes, and duration of trial based on continuously emerging knowledge throughout the study. Adjunctive therapy A therapy (e.g., medication, exercise, diet) that all subjects within a study receive. Since all subjects are receiving, it is not considered a study bias since the effect of this therapy occurs among all subjects within the study. Adverse drug event (ADE) An ADE is defined as an injury from a medicine or lack of intended medicine. An ADE refers to all adverse drug reactions (ADRs), including allergic or idiosyncratic reactions, as well as medication errors that result in harm to a patient. Adverse drug event (ADE) monitoring Computer programs that use electronic data and predetermined rules to identify when an ADE may have occurred or is about to occur.

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Adverse drug event (ADE) trigger tool It is an augmented chart review method that uses automated systems to identify alerts or triggers to efficiently identify patients with potential ADEs. The triggers are cues that a patient may have experienced an error and/or adverse event. When these triggers are identified, it is suspected that the patient may have experienced a medication error. The patient’s chart is reviewed for evidence of error and/or level of harm and data are collected and collated to determine potential common causes. Adverse drug reaction (ADR) Defined broadly, any unexpected, unintended, undesired, or excessive response to a medicine. The Food and Drug Administration's (FDA) definition of ADRs is: “any adverse event associated with the use of a drug in humans, whether or not considered drug related, including the following: adverse event occurring in the course of the use of a drug product in professional practice; an adverse event occurring from drug overdose, whether accidental or intentional; an adverse event occurring from drug abuse; an adverse event occurring from drug withdrawal; and any significant failure of expected pharmacologic action.” Adverse drug reactions also include drug interactions. Defined by the World Health Organization (WHO) as “any response that is noxious, unintended, or undesired, which occurs at doses normally used in humans for prophylaxis, diagnosis, therapy of disease or modification of physiological function.” Several other definitions are available; many of those are discussed in Chapter 15. Agenda for Change An initiative adopted by the JCAHO in 1986 intended to improve standards by focusing on key functions of quality of care, to monitor the performance of health care organizations using indicators, improve the relevance and quality of the survey process, and enhance the accuracy and value of JCAHO accreditation. Aggregate indicators Provide a summary of the frequency, or timeliness, of a process by aggregating numerous cases. Aggregator A piece of software that is used to automatically collect information from RSS and Weblog sites, which allows the user to look at material from many of those sites at one time and in one place. Alert fatigue A situation in which clinical decision support system (CDSS) alerts and warnings appear too frequently resulting in a provider ignoring or overriding these messages. Alert fatigue A situation in which clinical decision support system (CDSS) alerts and warnings appear too frequently resulting in a provider ignoring or overriding these messages. Alpha (level of significance) The probability of a false positive result in a study. Criterion for rejecting the null hypothesis. It is the upper bound of the Type I error rate. The value (usually 0.05) that is set prior to the beginning of a study and in which the p-value is compared at the end of the study to determine if statistical difference is present in the outcome being measured. A p-value less than alpha (e.g., 0.001 < 0.05) is interpreted as a statistical difference was determined between the intervention and control groups of a study. Also is the amount of error the researchers are willing to accept that a false-positive result is identified between the study intervention and control groups (e.g., alpha of 0.05 is up to a 5% chance of a false-positive result). Alternative hypothesis Sometimes referred to as the research hypothesis. The hypothesis that states there is some difference or relationship between the therapy under investigation and the control. Alternative medicine An approach to health care outside conventional medicine. Alternative medicine refers to using a nonmainstream approach in place of conventional medicine. American Hospital Formulary System (AHFS) classification A classification that can be found in the AHFS Drug Information reference book, published by the American Society of HealthSystems Pharmacists. It groups agents by use and/or drug class into specific numbered categories (e.g., 24:32.04 Angiotensin-Converting Enzyme Inhibitors).

GLOSSARY

1199

Analysis Analysis is the critical assessment of the nature, merit, and significance of individual elements, ideas, or factors. Functionally, it involves separating the information into its isolated parts so that each can be critically assessed. Analysis requires thoughtful review and evaluation of the quality and overall weight of available evidence. Analytic research Quantitative research conducted in a controlled environment to determine cause-and-effect relationships. Ancillary therapy A therapy (e.g., medication, exercise, diet) that is disproportional in use among the subjects in the groups within a study and has an effect on the outcome being measured; this can lead to a difference in effect between the groups, which can bias the study results. Antibiotic use review (AUR) Retrospective evaluation of antibiotic use. Usually quantitative and limited to identifying patterns of use. Application service provider (ASP) See Software as a Service (SaaS). Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument A structured instrument to assess the quality of guidelines, provide a methodological strategy for the development of guidelines, and inform what information and how information ought to be reported in guidelines. Article proposal A letter asking the publisher whether they would be interested in possibly publishing something on a particular topic written by the person(s) who are inquiring. Aspect of care A term used in quality assurance programs to indicate the title that describes the area being evaluated. Assay sensitivity The process of ensuring that a drug truly has a notable effect by utilizing highquality clinical trials of the drug against placebo. Assumption of the risk A defense in the law of torts, which bars a plaintiff from recovery against a negligent party if the defendant can demonstrate that the plaintiff voluntarily and knowingly assumed the risks at issue inherent to the dangerous activity in which he or she was participating at the time of his or her injury. Attributable risk The difference in the rate of a condition when comparing an exposed population to an unexposed population. This is primarily used in cohort studies and is considered an epidemiological term. Automated dispensing cabinets (ADCs) Automated devices with a range of functions. Core capabilities include medication storage and retrieval for administration to patients, especially in patient care areas, as well as audit trails of cabinet access. Other functions can include medication charging and automated inventory management. Autonomy Autonomy is the personal rule that is free from both controlling interferences by others and from personal limitations that prevent meaningful choice. Autonomous individuals act intentionally, with understanding, and without controlling influences. Avatar A movable three-dimensional image used to represent some body in cyberspace. Bar code verification The use of bar code scanning to ensure that the correct drug, strength, and dosage form were dispensed in the drug selection process and the five basic patient rights (i.e., right patient, right drug, right dose, right route, right time) are followed at the point of care. Beneficence A basic principle of consequentialist theory that expresses the duty to promote good.

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Berkson’s bias A type of selection bias noted in case-control studies and is produced when the probability of hospitalization of cases and controls differs. This probability can be increased based on the specific exposure being studied and increases the chance of hospital admission. Beta The probability of a false-negative result in a study. Bias An intentional or unintentional systematic error in the way a study is designed, conducted, analyzed, or reported. Bibliography A list of references, usually seen at the end of a piece of professional writing. Biocreep (also known as placebo creep) The phenomenon that results in the reference drug becoming no better than placebo in NI trials. This occurs when a somewhat inferior test drug is chosen as the reference drug for a future generation of NI trials. After multiple generations of this occurring, the final result is a future reference drug that is no better than placebo. Bioequivalence studies Research that evaluates whether products are similar in rate and extent of absorption. Biologics license applications (BLA) A biologics license application is a submission that contains specific information on the manufacturing processes, chemistry, pharmacology, clinical pharmacology, and the medical effects of the biologic product. It is a request for permission to introduce, or deliver for introduction, a biologic product into interstate commerce. Black letter rules Principles of law that are known generally to all and are free from doubt and ambiguity. Also known as hornbook law, since they are in a format that would probably be enunciated in a hornbook. Blinding A study technique used in research to reduce bias by having the study subjects and/or investigators not know which group the study subjects are assigned. Blog See Weblog. Body area network (BAN) A multidevice, interconnected computer system carried on a person. Sometimes referred to as a wearable computer. Boolean operators (logical operators) Words used to combine search terms (i.e., AND, OR, NOT) when using computerized databases. Case law The aggregate of reported cases; the law pertaining to a particular subject as formed by adjudged cases. Case report A descriptive record of a single individual (case report) in which the possibility of an association between an observed effect and a specific intervention or exposure is described (often an unexpected complication of treatment or procedure) based on detailed clinical evaluation and history of the individual. Case series A grouping of records (case studies) that documents a practitioner’s experiences, thoughts, or observations related to the care of multiple patients with similar medical situations. Case study A record of descriptive research that documents a practitioner’s experiences, thoughts, or observations related to the care of a single patient. Not useful to test a hypothesis but can serve to generate pilot information to design future controlled trials. Case-control study A retrospective trial design (often using medical charts or cases) strictly based on the observation of a group of people that have experienced a similar outcome. The study is used to determine the possible exposure these patients have had, which would lead to this known outcome. Categorical variable A variable measured on a nominal or ordinal scale.

GLOSSARY

1201

CD-ROM See Compact disc-read only memory. Civil liability Negligent acts and/or omissions, other than breach of contract, normally independent of moral obligations for which a remedy can be provided in a court of law. This form of liability is imposed under civil laws and processes, not criminal law. For example, a person injured in someone’s home can bring suit under civil liability law. Clinical decision support systems (CDSS) Computer programs that augment clinical decision making by combining referential information with patient-specific information to prevent negative actions and update providers of patient status. Clinical investigation Any experiment in which a drug is administered or dispensed to one or more human subjects. An experiment is any use of a drug (except for the use of a marketed drug) in the course of medical practice. Although there are many other definitions, this is the FDA’s definition and would seem the appropriate one to use given the nature of this topic. Please note that the FDA does not regulate the practice of medicine and prescribers are (as far as the agency is concerned) free to use any marketed drug for off-label use. Clinical practice guidelines Recommendations for optimizing patient care that are developed by systematically reviewing the evidence and assessing the benefits and harms of health care interventions. Clinical response letters Written correspondence that contains company-approved content in response to an unsolicited request for medical information. Clinical Safety Officer (CSO) Also known as the regulatory management officer (RMO). This will be the sponsor’s Food and Drug Administration contact person. Generally, the CSO/RMO assigned to a drug’s investigational new drug application will also be assigned to the new drug application. Clinical significance The clinical importance of data generated in a study, irrespective of statistical results. Usually refers to the application of study results into clinical practice. Also, can be called clinical meaningfulness. Clinically significant A result large enough to cause an effect on an efficacy outcome measure that noticeably changes a patient’s condition. Closed formulary A drug formulary that restricts the drugs available within an institution or available under a third-party plan. Coauthor Any individual who writes a portion of an article, chapter, book, etc. This includes individuals other than the primary author, whose name is normally listed first on a publication. Cohort study A prospective trial design strictly based on the observation of a group of people who experience a known exposure over time. The study is used to determine outcomes to this exposure. Commercial IND An IND for which the sponsor is usually either a corporate entity or one of the institutes of the National Institutes of Health (NIH). In addition, CDER may designate other INDs as commercial, if it is clear the sponsor intends the product to be commercialized at a later date. Community rule See Locality rule. Compact disc-read only memory (CD-ROM) A storage and retrieval system for large quantities of computerized data. Modern computers usually cannot only read the data on these disks, but usually can write new data to disks designed to accept that new data. Comparative negligence The allocation of responsibility for damages incurred between the plaintiff and the defendant, based on the relative negligence of the two.

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Complementary and alternative medicine An approach to health care outside conventional medicine. Complementary medicine refers to using a non-mainstream approach together with conventional medicine. Alternative medicine refers to using a non-mainstream approach in place of conventional medicine. Compliance A measure of how well instructions are followed. In a study, compliance refers to how well a patient follows instructions for medication administration and how well the investigator follows the study protocol. Computer network An interconnection of computers and computer-related devices (e.g., printers, modems) that allows the devices to interchange data, electronic mail, programs, and other files. In addition, a network allows sharing of peripheral devices, such as printers, modems, fax boards, etc. Normally, this interconnection is via a dedicated wiring system (other than telephone/ modem communication); however, wireless connections are becoming common. Computer-based clinical decision support systems software that is designed to assist clinical decision making which utilizes both patient-specific information and clinical knowledge to make assessments or recommendations in clinical practice. Computerized provider order entry (CPOE) A process allowing medical provider instructions to be electronically entered for the treatment of patients who are under a provider’s care. Concurrent indicator An indicator used in any quality assurance program that determines whether quality is acceptable while an action is being taken or care is being given. Concurrent negligence The wrongful acts or omissions of two or more persons acting independently, but causing the same injury. Confidence interval Range calculated for a study result in which the true value for the population exists; the percentage association with the range (e.g., 95%) indicates the confidence in which the true population value is within the range. For instance, the investigators are 95% confident that the mean blood pressure lowering effect of the medication for the population is between −8 to −12 mmHg for the 95% confidence interval of (−8 to −12 mmHg). Confidentiality A moral rule, related to the principle of autonomy, which specifically addresses the individual client’s right to give or refuse consent relative to release of privileged information. Conflict of interest A situation in which the interests of an investigator conflicts with the study purpose, design, and/or result interpretation. An investigator may be a stock holder of and/or speaker for a pharmaceutical company; the study may be designed to produce favorable results and/or be interpreted or promoted with a bias to use the study intervention. Confounder A known or unknown variable that has the potential to mask actual associations or falsely demonstrate a nonexisting but apparent association between the defined related variable (exposure) and outcome(s) being studied. The real issue occurs when the confounder is unevenly distributed between the exposure and nonexposure study groups, leading to confusion interpreting the results. Unevenly distributed confounders are common in cohort studies since this imbalance is the product of not using a randomization schedule that evenly distributes the confounder between the groups. Consent A moral rule related to the principle of autonomy which states that the client has a right to be informed and to freely choose a course of action. Consequential damages Also called special damages; damages claimed and/or awarded in a lawsuit which were caused as a direct foreseeable result of wrongdoing. Consequential damages occur with injury or harm that does not ensue directly and immediately from the act of a party, but only from some of the results of such act, and that is compensable by a monetary award after a judgment has been rendered in a lawsuit.

GLOSSARY

1203

Consequentialist theories An ethical theory which holds that the rightness or wrongness of decisions or actions is determined by the total of good that is achieved or harm that is prevented. Constancy assumption A determination made that past studies being used to determine the noninferiority (NI) margin in an NI trial are similar in design and conduct compared to the NI trial regarding features that could alter the effect size of the reference drug compared to placebo. Consumer health information (CHI) Information actively sought by the patient in response to their need for more information about their health. Information is not individualized for a specific patient but rather general health information. Content filter A term used to limit a search to specific things, such as specific drugs or disease states. Continuous indicators Provide a simple count, or time estimate, related to a process (e.g., average turnaround time on medication orders). Continuous quality improvement (CQI) The term given to the methodologies used in the process of Total Quality Management. Efforts to improve quality are part of each participant’s responsibilities on an ongoing basis. Continuous variable A variable measured on an interval or ratio scale. Contract research organization (CRO) An individual or organization that assumes one or more of the obligations of the sponsor through an independent contractual agreement. Control group The group of test animals or humans that receive a placebo (a dosage that does not contain active medicine) or active (a dosage that does contain active medicine) treatment. For most preclinical and clinical trials, the FDA will require that this group receive placebo (commonly referred to as the placebo control). However, some studies may have an active control, which generally consists of an available (standard of care) treatment modality. An active control may, with the concurrence of the FDA, be used in studies where it would be considered unethical to use a placebo. A historical control is one in which a group of previous patients is compared to a matched set of patients receiving the new therapy. A historical control might be used in cases where the disease is consistently fatal (i.e., acquired immunodeficiency syndrome [AIDS]). (Refer to Chapter 4 for additional information on control groups). Controlled clinical trial Research design that prospectively and directly compares plus measures and quantifies differences in outcomes between an intervention and control. This is the best study design to determine a cause-and-effect relationship between an item under investigation and an outcome. Controls A treatment (placebo, active, historical) used for comparison in a study to measure a difference in effect against an investigational agent. The investigator usually wishes to determine superiority of a new treatment over the control in terms of efficacy and safety. Copayment Payment made by an individual who has health insurance at the time the service is received to offset the cost of care. Copayments may vary depending on the service rendered. Cost-benefit analysis (CBA) A study where monetary value is given for both costs and benefits associated with a drug or service. The results are expressed as a ratio (benefit-to-cost), and the ratio is used to determine the economic value of the drug or service. Cost-benefit study A study where monetary value is given for both costs and benefits associated with a drug or service. The results are expressed as a ratio (benefit to cost), and the ratio is used to determine the economic value of the drug or service. Cost-consequence analysis (CCA) An informal variant of a cost-effectiveness analysis (CEA). The costs and various outcomes are listed but no evaluations are conducted.

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Cost-effectiveness analysis (CEA) A study where the cost of a drug or service is compared to its therapeutic impact. Cost-effectiveness studies determine the relative efficiency of various drugs or services in achieving desired therapeutic outcomes. Cost-effectiveness ratio (CER) The CER is the ratio of resources used per unit of clinical benefit, and implies that this calculation has been made in relation to doing nothing or no treatment. Cost-effectiveness study A study where the cost of a drug or service is compared to its therapeutic impact. Cost-effectiveness studies determine the relative efficiency of various drugs or services in achieving desired therapeutic outcomes. Cost-minimization study A study that compares costs of drugs or services that have been determined to have equivalent therapeutic outcomes. Cost-utility analysis (CUA) A study that relates therapeutic outcomes to both costs of drugs or services and patient preferences, and measures cost per unit of utility. Utility is the amount of satisfaction obtained from a drug or service. Cost-utility study A study that relates therapeutic outcomes to both costs of drugs or services and patient preferences and measures cost per unit of utility. Utility is the amount of satisfaction obtained from a drug or service. Covenant An ethical covenant in medical ethics suggests an implicit contract between the client and the health care provider that broadly describes the relationship involved whenever a health care service is provided, including the provision of information. Within this contract the service recipient has a right to competently provided service, as well as respectful treatment. The service recipient also has an obligation to provide needed information to the provider in a respectful manner. Coverage error It is a bias in a statistic that occurs when the target population you want to survey does not coincide with the sample population that is actually surveyed. This can be an issue when observing a sample of the population instead of the entire population. Coverage rules Criteria for specific drugs determined by the health plan in conjunction with the pharmacy and therapeutics committee that is used to determine if a prescription is covered. Criteria are based on evidence-based medicine. CQI See Continuous quality improvement. Criteria Definitions of safe and effective use of medications used to assess components of the medication use process that are endorsed by the organization within which they are to be applied. Criteria summarize an organization’s definition of appropriate or acceptable use of the medication. Crossover study A study where each subject receives all study treatments, and endpoints during the various treatments are compared. Cross-sectional Data that is measured one time. Cross-sectional study A trial design involving data collection only once on members of the study population that represents a snapshot in time. These members are not required to be studied all at once, but each member can be studied at a different time. The key is that the data collected represents a specific period in time. Cybermedicine A concept broader than telemedicine that includes the marketing, relationship creation, advice, prescribing, and selling pharmaceuticals and devices in cyberspace. Dechallenge In relation to adverse drug reactions, this occurs when the drug is taken away and the patient is monitored to determine whether the adverse drug reaction (ADR) abates or decreases in intensity.

GLOSSARY

1205

Decision analysis A tool that can help visualize a pharmacoeconomic analysis. It is the application of an analytical method for systematically comparing different decision options. Decision analysis graphically displays choices and performs the calculations needed to compare these options. Deep pocket Practical consideration that involves the naming of additional codefendants in personal injury lawsuits to provide assurance to the plaintiff that there will be sufficient assets to pay the judgment. Degrees of freedom The number of data points that are free to vary. Delta The amount of difference that the investigators wish to detect between intervention and control groups in a study. Deontological theory An ethical theory that seeks to establish what is a right or wrong decision or action on the basis of prioritizing specific recognized ethical rules or principles. Descriptive research Quantitative research that describes naturally occurring events. Descriptive statistics Statistics that describe data such as medians, modes, and standard deviations. Diagnostic review bias It occurs when the reference test results are not definitive and the study test results affect or influence how the final diagnosis is established. DIC See Drug information center. Dichotomous variable A variable that has two mutually exclusive categories. Digital video disk (DVD) Also known as digital versatile disk. A disk that physically resembles a CD-ROM, but allows the storage of much larger amounts of data. It requires a special reading/ writing device in a computer, although this device may also be combined with that used for CD-ROMs. DVDs have been used to a large extent to store and replay movies; however, it is being used on computers to store large amounts of computer data, particularly large multimedia files. Direct medical costs One of four categories of costs in pharmacoeconomic studies. These are the medically related inputs used directly in providing the treatment. Direct nonmedical costs One of four categories of costs in pharmacoeconomic studies. These are costs directly associated with treatment, but are not medical in nature. Examples include travel, food, and lodging to get to a place of treatment. DIS See Drug information service. Discount rate A term from finance which approximates the cost of capital by taking into account both the projected inflation rate and the interest rates of borrowed money, and then estimates the time value of money. Drug class review A drug evaluation monograph comparing all products in a particular class of drugs. It is used to determine what products will be available for use. Drug evaluation monograph A structured document covering all aspects of a particular drug product or class of drugs. It compares similar agents and is used to determine which products will be available for use. Drug formularies See Formulary. Drug formulary system See Formulary system. Drug informatics The electronic management of drug information.

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Drug information Facts or advice on drugs (including chemicals that has medicinal, performanceenhancing or intoxicating effects) regarding a specific patient or a group of patients. Drug information center (DIC) A physical location where pharmacists have the resources (e.g., books, journals, computer systems, etc.) to provide drug information. This area is generally staffed by a pharmacist specializing in drug information, but may be used by a variety of the pharmacy staff or other individuals. Drug information service A professional service providing drug information. This service is normally located in a drug information center. Drug interaction The Food and Drug Administration defines this as “a pharmacologic response that cannot be explained by the action of a simple drug, but is due to two or more drugs acting simultaneously.” Drug master file (DMF) A submission to the FDA that may be used to provide confidential detailed information about facilities, processes, or articles used in the manufacturing, processing, packaging, and storing of one or more human drugs. Drug product The final dosage form prepared from the drug substance. Drug promotion Information provided by the pharmaceutical industry through advertising, detailing, and other printed material intended to increase sales of a medication. Drug regimen review (DRR) The monthly evaluation of nursing home charts by pharmacists. Drug substance Bulk compound from which the drug product is prepared. An active ingredient that is intended to furnish pharmacological activity or other direct effect in the diagnosis, cure, mitigation, treatment, or prevention of disease or to affect the structure or any function of the human body. Drug use evaluation (DUE) Concurrent evaluation of prescribing and outcome only. Multidisciplinary involvement. Drug use review (DUR) A program related to outpatient pharmacy services designed to educate physicians and pharmacists in identifying and reducing the frequency and patterns of fraud, abuse, gross overuse, or inappropriate or medically unnecessary care. DUR is typically retrospective in nature and utilizes claims data as its primary source of information. Due care See Reasonable care. Duty A moral or legal obligation. Editorial A commentary usually written by an expert that describes study strengths and limitations plus the application of the study results to practice. This is published in the same journal issue as the study, but not all studies have an accompanying editorial. Electronic mail (e-mail) Brief messages sent from one computer to another, similar in use to interoffice memos. This serves as a quick, informal method of written communication. Also, e-mail may be used to send other items, such as word processing files, graphics, video, etc., to others. Electronic medication administration record (eMAR) An electronic version of the traditional medication administration record. It supports patient safety by incorporating clinical decision support and bar-coded medication administration. It also enables real-time documentation and billing of medication administration. Electronic prescribing (e-prescribing) Prescription entered by a prescriber directly into an electronic format using agreed-upon standards, which is securely transmitted to the pharmacy that the patient chooses. Faxes and printed prescriptions are not e-prescriptions.

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E-mail See Electronic mail. Endpoint A parameter measured in a clinical study. The primary endpoint is the major variable analyzed and reflects the main objective of the study. Secondary endpoints are additional variables of interest monitored during clinical studies. Endpoint, Primary See Primary endpoint. Endpoint, Secondary See Secondary endpoint. Error, Type I See Type I error. Error, Type II See Type II error. Ethical theories Integrated bodies of principles and rules that may include mediating rules that govern cases of conflicts. Ethics The philosophical inquiry of the moral dimensions of human conduct. An ethical issue involves judgments between right and wrong human conduct or praiseworthy and blameworthy human characters. Ethics (defined by AACP) Philosophical inquiry into the moral dimensions of human conduct. Ethics (defined by Beauchamp and Childress) A generic term for several ways of examining the moral life. Evidence-based medicine (EBM) A philosophy of practice and an approach to decision making in the clinical care of patients that involves making individual patient care decisions based on the best currently available evidence. Exclusion criteria Characteristics of subjects defined prior to starting the study that are used as parameters to disqualify subjects from enrolling in the study (e.g., patients with cancer, lactating females, patients receiving corticosteroid therapy). Exculpatory clause An exculpatory clause is part of an agreement which relieves one party from liability. It is a provision in a contract which stipulates (1) one party is relieved of any blame or liability arising from the other party’s wrongdoing, or (2) one party (usually the one that drafted the agreement) is freed of all liability arising out of performance of that contract. An exculpatory clause will not be enforced when the party protected by the clause intentionally causes harm or engages in acts of reckless, wanton, or gross negligence or when found to be unreasonable under the particular circumstances (e.g., a restaurant checks a person’s coat but the ticket states they are not responsible for loss or damage). Exploratory research Research of a qualitative nature in which the investigators examines an unknown area to generate hypotheses. Extemporaneous compounding The practice of compounding prescriptions from a list of several ingredients—usually performed by a pharmacist. External validity Quality of the study design that allows the result to be applied to practice. Study results are meaningful to practitioners and can be used for patient care. Failure mode and effects analysis (FMEA) FMEA is a structured proactive method of evaluating a process to identify the gaps—how the process might fail. The process includes identification of the likelihood of each of the failures along with its relative impact to the patient. This provides a prioritization of action plans to drive improvement and reduce the likelihood of failure. Fair balance A quality of drug promotions where similar attention is given to safety risks (e.g., contraindications, precautions/warnings, adverse effects) and efficacy benefits.

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False negatives Individuals with the disease who were incorrectly identified as being disease free by the test. False positives Individuals without the disease who were incorrectly identified as having the disease by the test. Fellowship A directed, highly individualized postgraduate training program designed to prepare the participant to function as an independent investigator. The purpose of fellowship training programs is to develop competency and expertise in the scientific research process, including hypothesis generation and development, study design, protocol development, grantsmanship, study coordination, data collection, analysis and interpretation, technical skills development, presentation of results, and manuscript preparation and publication. A fellowship candidate is expected to possess appropriate practice skills relevant to the knowledge area of the fellowship. Such skills may be obtained through prior practice experience or completion of a residency program. Fidelity A principle of moral duty in deontological theory that addresses the responsibility to be trustworthy and keep promises. File transfer protocol (FTP) A method to transfer files from one computer to another. Follow-up study A study where subjects exposed to a factor and those not exposed to the factor are followed forward in time and compared to determine the factor’s influence on disease state development. Also called a cohort study. Food and Drug Administration (FDA) The agency of the U.S. government that is responsible for ensuring the safety and efficacy of all drugs on the market. Forest plot A preferred method to display the results from a meta-analysis and includes the 95% confidence intervals for the primary efficacy outcome for each study included in the metaanalysis with the overall resulting 95% confidence interval for the meta-analysis. Formulary A continually revised list of medications that are readily available for use within an institution or from a third-party payer (e.g., insurance company, government) that reflects the current clinical judgment of the medical staff or the payer. Restrictions on this list may be placed that indicate certain drugs will not be reimbursed by insurance, or will only be reimbursed if several other alternative are tried first. Formulary decision supports (FDS) Program (often software) used to enhance compliance with formulary by guiding the prescriber to preferred formulary drugs over those considered nonformulary. Formulary system A method used to develop a drug formulary. It is sometimes even thought of as a philosophy. Funnel plot A scatter plot of treatment effect versus sample size of studies included in a metaanalysis. They are used to assist in detecting potential publication bias which is a form of selection bias based on the magnitude, direction, or statistical significance of the study results. Galley proofs A copy of a written work as it is to be published. The purpose of this document is to allow the author(s) to make a final check to ensure everything is correct before actual publication. Gantt chart Project management tool used to plan and monitor elements of a project. Gender bias Showing favoritism or discrimination toward a selected gender. Good clinical practice (GCP) A standard for the design, conduct, monitoring, analyses, and reporting of clinical trials that provides assurance that the results are credible and accurate, and that the rights of study subjects are protected.

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1209

Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) system A standardized system for grading the quality of the evidence and the strength of recommendations in clinical practice guidelines. Gray literature Documents provided in limited numbers outside the formal channels of publication and distribution. The concern with these documents in that they may include inaccurate information (not completely correct information), misinformation (incorrect information), and disinformation (false information deliberately provided in order to influence opinions) that can confound study results. Health 2.0 The utilization of health care related tools provided by Web 2.0. Health applications (apps) Software for devices designed to manage various aspects of health for the specific user. Health Insurance Portability and Accountability Act of 1996 Commonly referred to as HIPAA, this act includes privacy restrictions for electronic health records. Health literacy It is the capability of patients to read or hear health information, understand it, and then act on health information. Health maintenance organization (HMO) Form of health insurance whereby the member prepays a premium for the HMO’s health services, which generally include inpatient and outpatient care. Health Plan Employer Data and Information Set (HEDIS) A set of performance measures used to compare managed health care plans. Health product information Identified by Project Destiny as a service area where as pharmacists augment patient’s health care by providing information to patients based on information from prescription history, patient profiles, and purchases. Health-related quality of life (HR-QOL) Term used to represent the value assigned to quality and quantity of life that can be modified by various factors such as impairments and perceptions that are caused by disease, injury, treatment, or medical policy. Heterogeneity Noted in meta-analyses, this describes a situation where there are differences in the way the studies being included in the meta-analysis were conducted. Ideally, all studies included should be identical in design and the way they are conducted (same doses used, same inclusion/ exclusion criteria, same outcome measurements, same duration, etc.). There can be different degrees of heterogeneity; however, if there are significant variations in true effects underlying the studies, then the meta-analysis results are in question. HIPAA See Health Insurance Portability and Accountability Act of 1996. Hippocratic Oath A central ethical tradition of Western medicine that is committed to producing good for one’s patient and protecting that patient from harm. There is a special emphasis placed on the responsibility of the medical professional to the specific patient. Historical data Data used in research that were collected prior to the decision to conduct the study (e.g., medical records, insurance information, Medicaid databases). Historical evidence of sensitivity to drug effects (HESDE) In noninferiority trials this concept applies to appropriately designed and conducted past trials using the reference drug and regularly exhibiting the reference drug to be superior to placebo. HMO See Health maintenance organization.

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Homogeneity As used with meta-analyses, homogeneity measures the differences or similarities between the several studies included in the meta-analysis. Ideally, all studies included should be identical in design and the way they are conducted (same doses used, same inclusion/exclusion criteria, same outcome measurements, same duration, etc.). Homogenicity tests Tests used when conducting a meta-analysis to determine the similarity of studies whose results were combined for the analysis. Homoscedasticity In correlation and regression, the variability around the best fit line of the linear relationship is constant across all data points. http—hypertext transfer protocol A method by which information is encoded and transmitted on the World Wide Web. https A secure form of http, used to transmit confidential information, such as credit card numbers. Hyperlink Also called a link; it is a word, group of words, or image that can be clicked on to jump (link) to another place within the same document or to an entirely different document. When you move the cursor over a link in a Web site, the arrow will turn into a little hand. Hyperlinks are the most essential ingredient of all hypertext systems, including the Internet. Hypothesis The researchers' assumptions regarding probable study results. The research hypothesis or alternative hypothesis (HA) is the expectations of the researchers in terms of study results. The null hypothesis (HO) is the no difference hypothesis, which assumes equality amongst study treatments. The null hypothesis is the basis for all statistical tests and must be rejected in order to accept the research hypothesis.

In vitro Experiments conducted using components of an organism that have been isolated from their usual biological surroundings. These types of experiments are also referred to as “test tube experiments.” In vivo Experiments conducted in living organisms in their intact state. Incidence rate Measures the probability that a healthy person will develop a disease within a specified period of time. It is the number of new cases of disease in the population over a specific time period. Inclusion criteria Characteristics of subjects defined prior to starting the study that are used as parameters to enroll participants in the study (e.g., males and females between 50 and 75 years of age with a prior myocardial infarction). Incremental cost-effectiveness ratio (ICER) Is the ratio of the change in costs to incremental benefits of a therapeutic intervention or treatment. Independent data monitoring committee A group of experts that are independent of the ongoing clinical study they are monitoring. This group looks at the blinded data being generated by the study to determine if changes or adjustments should be made about how the study is being conducted. Indexing service A searchable database of biomedical journal citations. Indicator drug A drug that, when prescribed, may offer evidence that an adverse effect to a drug may have occurred. Pharmacists can then investigate further to determine whether there really was an adverse effect. Examples are found in Chapter 17. Indicators Measures or screens for quality and can focus on structure, process, or outcomes. Indirect medical costs One of four categories of costs in pharmacoeconomic studies. Indirect costs involve costs that result from the loss of productivity due to illness or death.

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Inference engine Also known as the reasoning engine, this forms the brain of the clinical decision support system, working to link patient-specific information with information in the knowledge base. It evaluates the available information and determines what to present to the user. Inferential statistics Statistics (i.e., parametric and nonparametric tests) that determine the statistical importance of differences between groups and allow conclusions to be drawn from the data. Informatics specialist An individual that has advanced medication information skills with a keen understanding of computer and information technology. Information therapy Evidence-based patient education and/or medical information presented at an appropriate time to most effectively assist the patient in making a specific health decision or change in their behavior. Informed consent The document signed by a subject, or the subject’s representative, entering into a trial that informs him or her of his or her rights as a research subject, plus potential benefits and risks of the trial. This document indicates that the person is willing to participate in the study. Inherent drug risks Are unique to the drug and usually identified in the package insert, but do not include probable or common side effects. Injunction A judicial remedy issues in order to prohibit a party from doing or continuing to do a certain activity. Institutional Review Board (IRB) A group of individuals from various disciplines (e.g., lay people, physicians, pharmacists, nurses, clergy) who evaluate protocols for clinical studies to assess risks to the research participants and benefits to society. Approval of a local IRB (i.e., an IRB located in the community in which the study is to be conducted) is necessary prior to initiation of a clinical study involving patients. Intangible costs One of four categories of costs in pharmacoeconomic studies. Includes such items as the costs of pain, suffering, anxiety, or fatigue that occur because of an illness or the treatment of an illness. Intelligent infusion (smart) pumps Infusion pumps containing software designed to help eliminate pump programming errors. Intention-to-treat (ITT) A study design technique used to include results of all subjects in the final analysis even when the subject does not complete the entire study. Intention-to-treat analysis Analysis of all subject results randomized in a clinical trial regardless of whether they completed or dropped out of the study. Interim analysis Evaluation of data at specified time points before scheduled termination or completion of a study. Internal validity Internal validity refers to the extent to which the study results reflect what actually happened in the study (i.e., appropriate and sound study methods). Internet A worldwide computer network. Interoperability The ability of disparate computer systems to exchange information in a manner that allows the information to be used meaningfully. Interval data Data in which each measurement has an equal distance between points, but an arbitrary zero (e.g., temperature in Fahrenheit). Interval scale A scale of measurement that has rank ordered data with meaningful distance between two ranks, but no natural zero.

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Interventional study A study where the investigator introduces a factor and examines the factor’s influence on certain variables or outcomes. Intranet A computer network with restricted access, as within a health-system. Inverse variance test A statistical test commonly used to combine continuous data in metaanalyses. Investigational device exemption (IDE) An approved IDE means that the IRB (and FDA for significant risk devices) has approved the sponsor’s study application and all requirements under 21CFR812 are met. It allows the use of a device in a clinical investigation to collect safety and effectiveness data. Investigational new drug A drug, antibiotic, or biological that is used in a clinical investigation. The label of an investigational drug must bear the statement: “Caution: New Drug-Limited by Federal (or United States) law to investigational use.” Investigational new drug application (IND) A submission to the FDA containing chemical information, preclinical data, and a detailed description of the planned clinical trials. Thirty days after submission of this document to the FDA by the sponsor, clinical trials may be initiated in humans (unless a clinical hold is placed by the FDA). When the FDA allows the studies to proceed, this document allows unapproved drugs to be shipped in interstate commerce. Investigator The individual responsible for initiating the clinical trial at the study site. This individual must treat the patients, ensure that the protocol is followed, evaluate responses and adverse reactions, solve problems as they arise, and ensure proper conduct of the study. JCAHO Joint Commission on Accreditation of Healthcare Organizations. See The Joint Commission. Joint and several liability Refers to the sharing of liabilities among a group of people collectively and also individually. If the defendants are “jointly and severally” liable, the injured party may sue some or all of the defendants together, or each one separately, and may collect equal or unequal amounts from each. Just Culture Just Culture is a term coined by David Marx, which is a structured accountability model that supports patient safety and a learning culture. It is intended to balance recognition and understanding of system contribution to errors with an understanding of human error concepts in order to facilitate an accountability process that is valued by leadership and staff. When applied consistently and fairly it is also a proactive approach to identifying gaps in system processes. Justice A concept that relates to fairness and tendering what is due, resource allocation, and providing that to which the individual is entitled. Key opinion leaders Health care professionals considered to be experts in their area by their peers. Key opinion leaders are often highly regarded for their expertise in publications, speaking engagements, and influential value in the medical community. Kurtosis Refers to how flat or peaked the curve appears. A curve with a flat or board top is referred to as platykurtic while a peaked distribution is described as leptokurtic. Language bias Occurs when only specific articles are included in a review or study that are published in a specific language such as the review author’s native language. The issue is that potentially important articles are eliminated from the review. Law Involves written rules set by the whole society, or its representatives, that address the responsibilities of that society’s members.

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Learned intermediary A doctrine of products liability law and personal injury law; the manufacturer of a prescription drug fulfills its duty to warn of potentially harmful effects of the drug by informing the prescribing physician and is not also obligated to warn the user. The prescribing physician acts as a learned intermediary between the manufacturer and the consumer and has the primary responsibility of warning patients of the hazards of prescribed pharmaceutical products. This doctrine is an exception to the rule that one who markets goods must warn foreseeable ultimate users of dangers inherent in their products. Letter-to-the-editor Comments from readers of a study or other article published in a journal. These are published in a latter issue of the same journal and usually have a reply from the original study/article author(s). Occasionally, short reports of a case or small study may be reported this way. Level of evidence A scale used to categorize the overall quality of a specific clinical trial. The reliability of the results can be inferred from the category given a trial. Life table methods In the context of cohort study life tables, data are collected by following patients throughout their lives or a specific duration of their life and then compiled into tables for such uses as survival or mortality analyses comparing exposed to nonexposed situations. Listserver A service offered by some e-mail systems that allows a member of the listserver to send an e-mail message to one particular Internet address where it will be sent to all members of the listserver. This acts as a dynamic distribution list for e-mail messages. Local area network (LAN) A group of computers connected in a way that they may share data, programs, and/or equipment over a small geographic area (e.g., building, department). Locality rule Legal doctrine created in the latter part of the nineteenth century that stated that the local defendant practitioner would have his or her standard of performance evaluated in light of the performance of other peers in the same or similar communities. Also known as community rule. Logical operator A term such as AND, OR, NOT, NEAR, or WITH that can be used in searching a computer database. Logit See Log-odds. Log-odds A linear transformation of probability. That is, probability is bounded between 0 and 1, logodds transform probability to a continuous scale ranging from − ∞ to + ∞. The log-odds become the dependent variable in logistic regression. Longitudinal Data that is measured repeatedly over time. Macro Level of decision-making sets policy for the health system, as a standard established for an entire profession, or through government as law/regulation for the society as a whole. Mail service drug program Program that provides free home delivery for up to a 90 day supply of maintenance prescription drugs. Mainframe computer A large centralized computer that is used via computer terminals or other devices. This term is becoming blurred as smaller computer systems gain greater capabilities. Managed care organization (MCO) Health care provider who contracts with participating providers to provide a variety of services to enrolled members. Mantel-Haenszel test Statistical test commonly used to combine categorical data in meta-analyses. Marginal (or incremental) cost-utility ratio It is the gain in a benefit from an increase, or loss from a decrease, in a good or service, such as the quality-adjusted life years (QALY). It is

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calculated to estimate the added cost for an added benefit, not calculated when the added benefit comes at a lower cost. Marginal cost-effectiveness ratio The additional cost of one unit expansion of a single intervention. Matching A statistical technique which is used to evaluate the effect of a treatment by comparing the treated and nontreated units in an observational study such as case-control studies. The goal is to identify a nontreated patient with similar observable characteristics for every treated patient (cases and controls are similar), so treatment effect can be measured more accurately. Material issue of fact Genuine issue of material fact is a legal term often used as the basis for a motion for summary judgment. A summary judgment is proper if there is no genuine issue of material fact and the movant is entitled to a judgment as a matter of law. Such a motion will be granted if the party making the motion proves there is no genuine issue of material fact to be decided. When the moving party makes a prima facie showing that no genuine issue of material fact exists, the burden shifts to the nonmoving party to rebut the showing by presenting substantial evidence creating a genuine issue MCO See Managed care organization. Mean (arithmetic mean) The most common measure of central tendency for data measured on an interval or ratio scale and is best described as the average numerical value for the data set. Calculated as the sum of the observations divided by the number of observations. The arithmetic average of a set of numbers. Meaningful Use A set of standards defined by the CMS as the use of certified EHR technology to (1) improve quality, safety, and efficiency, (2) engage patients and their families, (3) improve care coordination, as well as public and population health, and (4) maintain privacy and security of PHI. Measurement error Occurs when the collection of data is influenced by the interviewer or when the survey item itself is unclear from the respondent’s point of view. Measures of association Calculation and interpretation of nominal study results using relative risk (RR), relative risk reduction (RRR), absolute risk reduction (ARR), and numbers needed to treat (NNT). Median The absolute middle value of a set of number. Medical executive committee A committee that acts as the administrative body of a medical staff in an institution. It is responsible for overseeing all aspects of care within the institution. This committee may be known by other names at specific institutions. Medical Literature Analysis and Retrieval System (MedLARS) The computerized information retrieval system at the National Library of Medicine. Medical Subject Headings (MeSH terms) A thesaurus of official indexing terms used when searching some of the databases of the National Library of Medicine (e.g., MEDLINE, TOXLINE). Medication error Any preventable event that may cause or lead to inappropriate medication use or patient harm while the medication is in the control of the health care professional, patient, or consumer. Such events may be related to professional practice, health care products, procedures, and systems, including prescribing; order communication; product labeling, packaging, and nomenclature; compounding; dispensing; distribution; administration; education; monitoring; and use.

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Medication guides Information for drug and biological products that the FDA determines pose a serious and significant public health concern requiring the distribution of FDA-approved patient medication information that is necessary to patients’ safe and effective use of the drug products. Medication information Facts or advice on medicines regarding a specific patient or a group of patients. Medication misadventure Any iatrogenic hazard or incident associated with medications that is an inherent risk when medication therapy is indicated; is created through either omission or commission by the administration of a medicine or medicines during which a patient may be harmed, with effects ranging from mild discomfort to fatality; may be attributable to error (human or system, or both), immunologic response, or idiosyncratic response; is always unexpected or undesirable to the patient and the health professional; and whose outcome may or may not be independent of the preexisting pathology or disease process It includes adverse drug events (ADEs), adverse drug reactions (ADRs), and medication errors. Medication therapy management Medication therapy management is a distinct service or group of services that optimizes drug therapy with the intent of improved therapeutic outcomes for individual patients. Medication tier status An indication, usually designated by the payer for prescriptions, as to whether a medication is a preferred agent within its class (i.e., first tier), second choice (second tier), third choice (third tier), or not covered. Each “tier” is generally associated with decreasing payment from the payer and increasing patient responsibility for the cost of the drug, with “not covered” (or nonformulary) being entirely the patient’s responsibility to pay. Medication usage patterns Trends and patterns of drug use. Often influenced by reimbursement decisions and formularies of insurance companies, Medicare and Medicaid, direct-to-consumer advertising, etc. Medication use evaluation (MUE) A part of the overall performance improvement program within institutional settings that provides in-depth assessment of the medication use process including prescribing, dispensing, administering, monitoring, and outcome. Multidisciplinary involvement. MedLARS See Medical Literature Analysis and Retrieval System. MedWatch The FDA Medical Products Reporting Program that monitors clinically significant adverse drug events and problems with medical products. Information is found at http://www .fda.gov/medwatch. Meso Level of decision making variably described as occurring at the institutional/organizational level or at community/regional levels of health care. Meta-analysis A study design where results of previously conducted similar clinical trials are combined, statistically analyzed, and new data are created for interpretation. Meta-analyses are especially useful when previous studies are inconclusive or controversial. They are also useful where sample size of multiple similar studies are too small to detect a statistically significant difference, but combining them will provide adequate sample size to meet a set power. Micro Level of health care related decision making, which involves decisions made at the individual professional–patient level of health care. Middle technical style A writing style used by professionals addressing professionals in other fields. It tends to be formal and avoids use of the first person (e.g., I, us). Technical jargon is avoided in this writing style.

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Mode The most frequently occurring value or category in the set of data. A data set can have more than one mode. Modified systematic approach A seven-step approach to answering drug information requests that includes (1) secure demographics of requestor, (2) obtain background information, (3) determine and categorize ultimate question, (4) develop strategy and conduct search, (5) perform evaluation, analysis, and synthesis, (6) formulate and provide response, and (7) conduct follow-up and documentation. Morbidity Detrimental consequences (other than death) related to a treatment, exposure, or disease state. MTM See Medication therapy management. MUE See Medication use evaluation. Multicollinearity Two or more variables have extremely high correlations (e.g., > 0.90) indicating that they are redundant or that they are measuring the same construct. Narrative review See Nonsystematic review. Narrow therapeutic index Used to describe a drug with small differences in dose or blood concentration that may lead to dose and blood concentration dependent serious therapeutic failures or adverse drug reactions. National Committee for Quality Assurance (NCQA) An organization dedicated to assessing and reporting on the quality of managed care plans; it surveys and accredits managed care organizations much like JCAHO accredits hospitals. National Patient Safety Goals (NPSGs) Program established and updated by The Joint Commission to assist health care organizations to address safety concerns related to patient safety. NCQA See National Committee for Quality Assurance. Negative formulary A drug formulary that starts out with every marketed drug product and specifically eliminates products that are considered inferior, unnecessary, unsafe, too expensive, and so forth. Negligence Failure to exercise that degree of care that a person of ordinary prudence or a reasonable person would exercise under the same circumstances. Elements of a negligence case include (1) duty breached, (2) damages, (3) direct causation, and (4) defenses absent. Negligent misrepresentation Occurs when the defendant carelessly makes a representation or statement without reasonable basis to believe it to be true. The burden of proof that is required passes to the person who made the statement who must prove that the statement was either not one of fact but opinion and that he or she had reasonable ground to believe and did believe that the facts represented were true. Network meta-analysis Also referred to as a multiple treatment comparison meta-analysis or mixed treatment meta-analysis, it is a network of randomized controlled trials which is developed where all these trials have one intervention in common. This network allows an indirect estimate for comparison of interventions A and B when head-to-head trials do not exist. New drug application (NDA) The application to the FDA requesting approval to market a new drug for human use. The NDA contains data supporting the safety and efficacy of the drug for its intended use. New molecular entity (NME) A compound that can be patented and has not been previously marketed in the United States in any form.

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NNT See Number needed to treat. N-of-1 Study A controlled study conducted in a single subject where periods of exposure to a treatment are compared to periods of exposure to a placebo or alternative therapy, such as standard of care, to determine the effects of the treatment on various variables and outcomes in the subject. Nominal data Data that are categorical (e.g., yes/no; male/female). Nominal scale A scale of measurement that places data into mutually exclusive categories without reference to rank order. Noninferiority Any difference shown in a noninferiority design trial between two treatments is small enough to conclude the test drug has an effect not too much smaller (no worse) than the active control or reference drug. Noninferiority margin A prespecified amount of effect used to show the test drug’s treatment effect is not worse than the reference drug by more than this specific degree. Noninherent drug risks Are created by the particular drug in combination with some extrinsic factor that the pharmacist should reasonably know about. Nonmaleficence A basic principle of consequentialist theory which encompasses the duty to do no harm. Nonparametric statistics Statistical tests used to analyze data that is not normally distributed such as nominal and ordinal data. Nonparametric tests Statistical tests that do not assume a conditional normal distribution. Nonpharmacologic treatment Treatment options focusing on a holistic approach to patient care (e.g., nutrition and exercise-related interventions). Nonpublic unsolicited requests A nonpublic unsolicited request is an unsolicited request that is directed privately to a firm using a one-on-one communication approach. Nonresponse bias A type of bias called nonresponse bias where the answers of respondents differ from the potential answers of those who did not answer the survey. This is the result of a nonresponse error associated with the survey. Nonresponse error Occurs when a significant number of subjects in a sample do not respond to the survey. The potential result of this is a type of bias called nonresponse bias where the answers of respondents differ from the potential answers of those who did not answer. Nonsystematic review A review article that summarizes previously conducted research, but does not provide a description of the systematic methods used to identify the research included in the article. Also called a narrative review. NPSG See National Patient Safety Goals. Null hypothesis The hypothesis that states there is no difference or relationship in the data. Statement of no difference in outcome between the intervention and control; created before the beginning of a study. This statement is either rejected or failed-to-be-rejected (i.e., accepted) at the end of the study based upon the p-value compared to the alpha value. Number needed to treat A measure used to determine the effectiveness of an intervention. This measure states the average number of patients who need to be treated with the intervention to prevent one additional negative outcome such as a myocardial infarction. The higher the value, the less effective is the intervention.

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OBRA ’90 See Omnibus Reconciliation Act of 1990. Observational study A study where the investigator analyzes naturally occurring events. Observer bias Tendency for observer/investigator to consciously or unconsciously distort what they see and record as the effect in a clinical trial situation. In other words, seeing what they expect to see. Odds ratio A measure of association between an exposure and an outcome. This ratio represents the chances that an outcome will occur given a particular exposure compared to the chances of the outcome occurring in the absence of that exposure. Off-label information Information that originates from sources (e.g., clinical studies, case reports) outside of the FDA-approved prescription drug labeling. Omission negligence Consulting the correct source, but failure to locate the correct answer(s) when providing information. Omnibus Reconciliation Act of 1990 (OBRA ’90) A statute (Public Law 101-508) focused on drug benefits provided under Medicaid. The statute requires pharmacists to conduct drug utilization review (DUR) including prescription screening, patient counseling, and documentation of interventions. Omnibus test A statistical test of an overall difference. That is, the test indicates if at least one difference or relationship is statistically significant. One-tailed test A hypothesis that makes claim to the direction of the difference or relationship. Online The process of connecting to a remote computer via modem or network. Open formulary A formulary that allows any marketed drug to be ordered in an institution or under a third-party plan. Can be considered an oxymoron. Ordinal data See Ordinal scale. Ordinal scale A scale of measurement that has rank order, but makes no reference to the distance between ranks. Ordinary care See Reasonable care. ORYXTM A JCAHO initiative to mandate the use of performance measurement tools to monitor outcomes and integrate these data into the accreditation process. Outcome indicators Quality assurance indicators that review whether the final desired result was obtained from whatever action was being reviewed. Outcomes A change in a patient’s health status (e.g., recovery, death, disability, disease, discomfort, and dissatisfaction) that can be attributed to the care provided. Outcomes research A systematic investigation which seeks to provide evidence about which interventions are best for certain types of patients and under certain circumstances. An attempt to identify, measure, and evaluate the end results of health care services. It may include not only clinical and economic consequences, but also outcomes, such as patient health status and satisfaction with their health care. Overview A general term for a summary of the literature. It includes nonsystematic (narrative), systematic (qualitative), and quantitative (meta analyses) reviews.

p value The probability of obtaining a test statistic as large or larger than the one actually obtained, conditional on the null hypothesis being true. It is the remaining area under a given probability distribution.

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P&T committee See Pharmacy and therapeutics committee. Pair-wise meta-analysis Traditional method to compile a meta-analysis by synthesizing the results of different trials to obtain an overall estimate of the treatment effect (one intervention relative to the control). The key is that all trials have the same intervention. All trials used are comparing treatment A to treatment B. Parallel forms A technique to test reliability of responses to questions in a survey by the responders. The technique usually consists of alternatively worded survey items placed throughout the survey. The parallel concept applies in that a question is asked in a positive manner in one place and in a negative manner in another place of the survey instrument. The answers to these alternate questions undergo a correlation analysis to determine the relatedness of the respondents alternate answers. Parallel study A study where two or more groups receive different treatments and the outcomes are compared. Parameter A measurement that describes part of the population. Parameter negligence Failure to consult the correct source in providing information. Parametric statistics Statistical tests used to analyze data with a normal (e.g., bell-shaped) distribution. Commonly used to analyze ratio and interval data. Parametric tests Statistical tests that assume a conditional normal distribution. Parenteral admixtures Solutions containing drug products for intravenous administration. Patient education Delivers written or verbal drug information through a planned activity initiated by a health care provider with the goal of changing patient behavior, improve adherence, and ultimately improve health. Patient pocket formulary Pocket-sized drug formulary listing top therapeutic drug classes, preferred products within those classes, cost index for the products, and other pertinent information. Patient Safety Organizations (PSO) The Patient Safety and Quality Improvement Act of 2005 (Patient Safety Act) authorized the creation of a nationwide network of Patient Safety Organizations (PSOs) to improve safety and quality through the collection and analysis of data on patient events. Patient-centered medical home (PCMH) Team-based healthcare delivery led by a physician or other primary care provider, but including multiple other types of providers, including pharmacists, to optimize patient outcomes. PBM See Pharmacy benefit management companies. Peer review A quality assurance program that centers on the evaluation of specific individuals by other similar professionals. Also, the process where a group of experts review a manuscript for accuracy and appropriateness for publication in a biomedical journal. Per protocol analysis Assessment of the study results in only those subjects completing the entire study duration. Performance indicators Items used to measure quality as part of the check function of quality improvement. The indicators typically focus on the process or outcomes of a care system, although they can also focus on structure. Per-protocol A study design technique to analyze the study results of only those subjects who completed the entire duration of the study.

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Personal health records (PHRs) Health 2.0 applications that provide the patient increased opportunity to participate in the collection, maintenance, and sharing of their personal healthrelated information through a Web-based environment. Perspective A pharmacoeconomic term that describes whose costs (such as the insurer, or the patient) are relevant based on the purpose of the study. Pharmaceutical care The responsible provision of drug therapy for the purpose of achieving definite outcomes that improve a patient’s quality of life. Pharmacoeconomics The description and analysis of the costs of drug therapy to health care systems and society—it identifies, measures, and compares the costs and consequences of pharmaceutical products and services. Pharmacoepidemiologic studies A type of study that combines clinical pharmacology and epidemiology design concepts to study the use of and the effects of drugs in large numbers of people. Pharmacovigilance The process of preventing and detecting adverse effects from medications. Pharmacy and therapeutics (P&T) committee A group in an institution or company that oversees any and/or all aspects of drug therapy for that institution or company. In hospitals, it is usually a subcommittee of the medical staff. May be known by a variety of similar names, such as pharmacy and formulary committee, drug and therapeutics committee (DTC), or formulary committee. Pharmacy benefit design Contract that specifies the level of coverage and types of pharmaceutical services available to the health plan member. Pharmacy benefit management (PBM) companies Organizations that manage pharmaceutical benefits for managed care organizations, medical providers, or employers. Pharmacy informatics Focuses on the use of information, information systems, and automation technology to ensure safe and effective medication usage. Pharmacy network Select pharmacies and pharmacy chains where members of a health plan have to go to get their prescriptions filled, usually at a lower cost. Placebo A pharmaceutical preparation that does not contain a pharmacologically active ingredient, but is otherwise identical to the active drug preparation in terms of appearance, taste, and smell. Placebo creep (also known as biocreep) The phenomenon that results in the reference drug becoming no better than placebo in noninferiority trials. This occurs when a somewhat inferior test drug is chosen as the reference drug for a future generation of NI trials. After multiple generations of this occurring, the final result is a future reference drug that is no better than placebo. Placebo effect A phenomenon where the patient has a perceived or actual improvement in their medical condition after receiving placebo treatment. Poison information A specialized area of drug information. By definition, it is the provision of information on the toxic effects of an extensive range of chemicals, as well as, plan and animal exposures. Poison information center A place that specializes in research, management, and dissemination of toxicity information. A physician usually directs it, although a pharmacist directs many on a day-to-day basis. Often, pharmacists and nurses provide staffing of these centers. Policy A broad, general statement that takes into consideration and describes the goals and purposes of a policy and procedure document.

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Popular technical style A writing style used by professionals addressing lay people. This is less formal than writing addressed to professionals. Population Every individual in the entire universe with the characteristics or disease states under investigation. Since entire populations are generally very large, a sample representative of the population is usually selected for an investigation. Positive formulary A drug formulary that starts out with no drug products and specifically adds products, after appropriate evaluation, that are needed by the institution or company.

Post hoc In clinical trial design, this distinguishes something that is done after the study is completed. Postgraduate year one (PGY1) residency An organized, directed, accredited program that builds on knowledge, skills, attitudes, and abilities gained from an accredited professional pharmacy degree program. The first-year residency program enhances general competencies in managing medication use systems and supports optimal medication therapy outcomes for patients with a broad range of disease states. Postgraduate year two (PGY2) residency An organized, directed, accredited program that builds on the competencies established in postgraduate year one of residency training. The second-year residency program is focused on a specific area of practice. The PGY-2 program increases the resident’s depth of knowledge, skills, attitudes, and abilities to raise the resident’s level of expertise in medication therapy management and clinical leadership in the area of focus. In those practice areas where board certification exists, graduates are prepared to pursue such certification. Postmarketing surveillance The process of continually monitoring and reviewing suspected adverse reactions associated with medications once they reach the market and are available to the public. Legislation mandates this activity for pharmaceutical manufacturers and the U.S. Food and Drug Administration (FDA), but health care providers can also participate by reporting adverse drug events, adverse drug reactions, and medication errors to the FDA and other regulatory bodies. Postmarketing surveillance study A study designed to examine drug use and frequency of side effects following approval by the Food and Drug Administration (FDA). Power The ability of a study to detect a difference between a study intervention and control if a difference exists. Usual minimum target value is 80%; power increases by increasing sample size, which also decreases the probability of a Type II or beta error. Power analysis A statistical procedure conducted by the investigators to determine a sample size for the trial. Preferred drug product Specific drug product within a specific therapeutic class selected as the most appropriate to treat a specific disease or condition as determined by the pharmacy and therapeutics committee. Preferred therapeutic class Specific drug class selected as the most appropriate to treat a specific disease or condition as determined by the pharmacy and therapeutics committee. Prescribability The ability of a drug to be prescribed for the first time. Prevalence Measures the number of people in the population who have the disease at a given time.

Prima facie A fact presumed to be true unless it is disproved. That is, evidence that is sufficient to raise a presumption of fact or to establish the fact in question unless rebutted.

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Primary author The author listed first on a publication. Sometimes referred to as the first author. Primary endpoint An outcome measured by the study investigators that quantifies the difference in effect between the intervention and control of the clinical trial. The results of this outcome measurement are used to answer the primary study objective. This outcome is also used by the study investigators to determine other study methods (e.g., sample size, statistical tests, duration, dose, patient type to enroll). Primary literature Original research published in biomedical journals. Principles In ethical analysis, a principle is relatively broad and fundamental in scope, and guides ethical decision-making or actions. Prior authorization Authorization from the health plan or pharmacy benefit manager in conjunction with the pharmacy and therapeutics committee for specified medications or specified quantities of medications. Request is reviewed against preestablished criteria which are based on evidence-based medicine. Privacy A rule within the principle of autonomy, more generally relating to the right of the individual to control his or her own affairs without interference from or knowledge of outside parties. Probabilistic sensitivity analysis A sensitivity analysis that allows one to determine how the results of an analysis would change when these best guesses or assumptions are varied over a relevant range of values. In cost-effectiveness analysis, probability distributions are created for each factor about which there is uncertainty. By simulating the results of random samplings from these distributions, it enables judgments to be formed about the decisions in relation to each factor. Procedures Specific actions to be taken. Process Refers to the set of activities that occur between the patient and the provider, encompassing the services and products that are provided to patients and the manner in which the services are provided. Process change Making a change to routine process. It may be through changes in policy or procedures, implementation of new services, acquisition of new equipment, changes in staffing, generation of regular notifications, or other methods. It is used to correct practice when quality assurance/drug usage evaluation/medication usage evaluation shows a deficiency. Process indicators Quality assurance indicators based on the presence or absence of policies and procedures. These assume that if policies and procedures are appropriate they will be effective and be properly performed. Process mapping A workflow diagram that provides a clear and consistent understanding of the steps and/or parallel processes required to accomplish a task. This is a process undertaken in many industries as an approach to developing standardized work as part of process improvement. It generally identifies variations within a process that may contribute to excess waste or risk. Product label The information affixed to the product and used to identify the contents. Product labeling Product information including prescribing information. Professional ethics Rules of conduct or standards by which a particular group in society regulates its actions and sets standards for its members. Professional writing Any written communication prepared in the fulfillment of the practice of a profession.

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Programmatic research Research focused on the impact and economic value of programs and services provided by pharmacists in community and institutional settings. Project management A discipline or science that is goal oriented, organized, detailed, and has built-in accountability. Prospective indicator An indicator used in any quality assurance program that determines whether quality is acceptable before an action is taken or care is given. Prospective study A study where data are collected forward in time from date of study initiation. Protected Health Information (PHI) A term under the HIPAA Privacy Rule which refers to individually identifiable health information that can be linked to a particular person. Specifically, this information can relate to the individual’s past, present, or future physical or mental health or condition, the provision of health care to the individual, or past, present, or future payment for the provision of health care to the individual. Common identifiers of health information include names, social security numbers, addresses, and birth dates. Protopathic bias When a treatment for the first symptoms of a disease or other outcome appears to cause the outcome. In this case, the first symptoms of the outcome of interest are the reason for the treatment under study and not the outcome itself. For instance, early symptoms of pancreatic cancer can be the symptoms of diabetes since beta cells are being destroyed by the cancer. Proximate cause Cause which immediately precedes and produces the effect, as distinguished from the remote or intervening cause. Public unsolicited requests An unsolicited request made in a public form, whether directed to a firm specifically or a forum at large. Publication bias To selectively pick publications and not include all publications available on the topic for the article. Pure technical style A writing style used by professionals addressing other professionals in the same field. It tends to be formal and avoids use of the first person (e.g., I, us). Technical jargon can be used in this writing style. Push technology A method by which information is actively sent to users’ computers with little, if any, effort required by the user. The information may be displayed as a screen saver or the computer may in some way let the user know that the information is available to be displayed (e.g., pop up notification). p-value A statistical value calculated based on the study results. When this number is less than the α-value, it is interpreted as the probability of rejecting a true null hypothesis or the probability of chance being the reason that a difference in the results between the two groups was calculated. QR (Quick Response) Codes A type of two-dimensional bar code that provides more information than is possible with a standard UPC barcode. It is increasingly common to find these codes on advertisements or items available for purchase, where the code can be scanned by an application on a smartphone to provide referral to a Web site where further information may be found on something of interest. Qualitative systematic review See Meta-analysis or Systematic review. Quality A degree or grade of excellence that can be applied to goods, services, processes or even people. Quality assessment and assurance committee A committee found in long-term care facilities to evaluate quality of care, including drug usage evaluation.

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Quality assurance A process used to ensure that something is done or made well enough. It is usually retrospective and focuses only on a particular component within a process, not the entire process. Quality measure When this term is used in health care, it is used to indicate methods of quantifying the type of care a patient received (indicators may include details about processes and organization structure, overall patient outcomes, and even patients’ perceptions of the quality of the care received). Quality of life This is an evaluation of a patient’s living situation based on the patient’s environment, family life, financial situation, education, and health. It is used in quality assurance programs when developing indicators. In some cases, quality-of-life aspects will take precedence over the absolute best treatment. For example, a quick cure to a disease state may not be as desirable when it costs so much that a family is bankrupted in the process. Quality-adjusted life years (QALY) A QALY is a health-utility measure combining quality and quantity of life, as determined by some valuations process. Quantitative systematic review See Meta-analysis or Systematic review. Quantity limits Set quantity of drug that can be prescribed, which is set by the health plan in conjunction with the pharmacy and therapeutics committee and is usually based on FDA-prescribing guidelines. Random error See Sample error. Randomization A process used in a study in which all subjects enrolled in the study have an equal opportunity to be in any of the study groups. This is used to reduce bias, enable the groups to be as similar as possible at baseline, and is required to validate certain statistical tests. Randomized clinical trial See Controlled clinical trial. Range The difference between the highest data value and the lowest data value. Rate-based indicators Usually measure the proportion of activities, or patients, that conform to a desired standard (e.g., the proportion of stat orders that are dispensed within 15 minutes). Ratio data Rank ordered data in which each measurement has a meaningful equal distance between points and also an absolute/natural zero (e.g., temperature in Kelvin). Ratio scale A scale of measurement that has rank ordered data with meaningful distance between ranks and a natural zero. Reasonable care Also called due care or ordinary care; conduct that an ordinarily prudent or reasonable person would normally exercise in a particular situation to avoid harm to another, taking the circumstances into account. The concept of due care is used as a test of liability for negligence and usually made on a case-by-case basis where each juror has to determine what a reasonable man or woman would do. Reasoning engine See Inference engine. Rechallenge In relation to adverse drug reactions, this occurs when the drug is discontinued and, after the adverse drug reactions (ADR) abates, the patient is given the same medication in an attempt to elicit the response again. Referee An expert in a particular area who reviews a written document to determine whether it is appropriate for publication.

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Refereed publication A publication in which the editors have experts in the appropriate field review items submitted for possible publication to determine whether those items are of suitable quality. Regulatory project manager (RPM) This is the sponsor’s primary FDA contact person. Each application that is submitted is assigned a regulatory project manager (RPM). Contact information for the RPM is provided in the letter sent to the applicant acknowledging receipt of the application. If the RPM is changed during the course of the review, the applicant is notified by the new RPM. Relative risk The risk of developing a disease or adverse event in those participants exposed to a specific variable compared to those not exposed to that variable. REMS See Risk evaluation and mitigation strategy. Research hypothesis (also known as the alternative hypothesis) It is a difference between the therapy under investigation and the control. Residual value The difference between the model predicted dependent variable and the actual dependent variable value.

Respondeat superior Refers to the proposition that the employer is responsible for the negligent acts of its agents or employees. Response bias See Measurement error. Restatement (Second) of Torts “An attempt by the American Law Institute to present an orderly statement of the general common law of the United States, including in that term not only the law developed solely by judicial decision, but also the law that has grown from the application by the courts of statutes...” It takes into account other factors, such as the modern trend of the law according to influential jurisdictions and well thought out opinions. Retrospective indicator An indicator used in any quality assurance program that determines whether quality was acceptable after an action was taken or care was given. Retrospective study A study that analyzes historical data (e.g., previously collected data such as medical records or insurance information). Risk Evaluation and Mitigation Strategy (REMS) A risk management plan required by the FDA that goes beyond requirements in the drug prescribing information to manage serious risks associated with a drug. Risk minimization action plans (RiskMAPs) A strategic safety program designed to meet specific goals and objectives in minimizing known risks of a product while preserving its benefits. RiskMAPs See Risk minimization action plans. Robustness The ability of the statistical test to produce correct test statistics and parameter estimates in the presence of assumption violations. Root cause analysis (RCA) In response to a sentinel or serious event, the expectation of The Joint Commission is that the organization will conduct a timely, thorough, and credible analysis to determine root causes of the event, develop and implement an action plan to reduce the risk of recurrence, and monitor the effectiveness of the plan and its implementation. RSS This acronym has multiple meanings, but is usually defined as Really Simple Syndication. It is a method by which an aggregator program collects information from Web sites and Weblogs (blog), which is then displayed as a collation. This allows individuals to monitor new or additional information on the Internet without having to use a browser to go to multiple Web sites.

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Rule In ethical analysis, a rule guides ethical decision making or actions, but is relatively specific in context and restricted in scope. Rules engines Computer programs, similar to ADE monitoring systems, with built-in, logic rules designed to aid in monitoring specific aspects of patient care. Run-in phase A short duration of patient assessment but prior to being enrolling in the study. Various reasons exist for this phase and include assessment of medication compliance, meet an inclusion criteria (e.g., LDL-C less than 130 mg/dL), and allow for medication washout. Sample A group of subjects, taken from the population, who are enrolled in a study; these individuals should represent the population in order for the study results to be extrapolated to the population. Sample frame A term describing the population that will actually be drawn from to make up the sample. Sample precision A measure of how close an estimator is expected to be to the true value of a parameter. Sample size The number of subjects in a study. Sampling bias See sampling error. Sampling error In statistics, this is incurred when the statistical characteristics of a population are estimated from a subset or sample of that population. The statistics on that sample such as means and variances differ from the parameters of the entire population because not all members of the entire population are used (also known as sample error). Secondary endpoint An outcome measured by the study investigators that quantifies the difference in effect between the intervention and control of the clinical trial, but not considered the focus of the study. The results of this outcome measurement are used to answer secondary study objectives. For example, a study compares a statin to placebo to determine if the statin can reduce the risk of having a stroke (primary endpoint); change in LDL-C levels are also compared between the two groups (secondary endpoint). Secondary literature Resources that index and/or abstract literature from biomedical journals. Selection bias Study subjects who meet the study inclusion/exclusion criteria but are not randomized into either the intervention or control (i.e., excluded from the study). Selection bias An error in selection method to obtain individuals or groups to take part in a scientific study. SEM See Standard error of the mean. Sensitivity The probability that a diseased individual will have a positive test result. It is the true positive rate of the test. The ability of a test to correctly identify those with the disease. Sensitivity analysis Tests that are undertaken to determine the influence of various criteria or conditions on study results. Sensitivity analyses are commonly used in meta-analyses and pharmacoeconomic research. Sentinel event The Joint Commission defines a sentinel event as an unexpected occurrence involving death or serious physical or psychological injury, or the risk thereof. Serious injury specifically includes loss of limb or function. The phrase “or the risk thereof” includes any process variation for which a recurrence would carry a significant chance of a serious adverse outcome. Such events are called sentinel because they signal the need for immediate investigation and response.

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Sentinel indicators Reflect the occurrence of a serious event that requires further investigation (e.g., adverse drug-related event, death). Skewness The measure of symmetry of a curve. Smartphone A cellular device that has Internet capabilities and supports various software functions. Social media Form of electronic communication allowing interactions with users to share information, messages, and other various forms of content. Software as a service (SaaS) (also known as application service provider [ASP]) A computing model in which an organization’s data and software are hosted by an off-site vendor who is responsible for maintaining data storage as well as the equipment on which it is stored. Special Protocol Assessment Is a statement from the U.S. Food and Drug Administration that an uninitiated or ongoing Phase III trial’s design, clinical endpoints, and statistical analyses are adequate for FDA approval. Specificity The probability an individual without a disease will have a negative test result. Sponsor An organization (or individual) that takes responsibility for and initiates a clinical investigation. The sponsor may be an individual or pharmaceutical company, government agency, academic institution, private organization, or other organization. Sponsor-investigator An individual who both initiates and conducts a clinical investigation (i.e., submits the IND and directly supervises administration of the drug as well as other investigator responsibilities). Stability study A study designed to determine the stability of drugs in various preparations. Stakeholder Any individual who affects or is affected by the problem or issue addressed by the policy. Standard A term used in quality assurance program that indicates how often an indicator must be complied with. The level of compliance will be set at either 0% (i.e., never done) or 100% (i.e., always done). A threshold, which allows compliance of between 0% and 100%, has sometimes been used instead of a standard. Standard deviation (1) A measurement of the range of data values (i.e., variability) around the mean. (2) The measure of the average amount by which each observation in a series of data points differs from the mean. In other words, how far away is each data point from the mean (dispersion or variability) or the average deviation from the mean. Standard error of the mean (SEM) An estimate of the true mean of the population from the mean of the sample. Mathematically, SEM is calculated as the standard deviation divided by the square root of the sample size. Ninety-five percent of the time, true mean of the population lies within +2 standard errors of the sample mean. Describes the precision of the mean of a sample of data that is being used to estimate some unknown or “true” value of the mean of the target population. The standard error of the mean (SEM) increases as the variability of the data increases, and decreases as the sample size increases. Standard gamble One method used in measuring health preferences. Each subject is offered two alternatives. Alternative one is treatment with two possible outcomes: either the return to normal health or immediate death. Alternative two is the certain outcome of a chronic disease state for life. The probability of dying is varied until the subject is indifferent between alternative one and alternative two. Used to assess his or her quality-adjusted life years (QALY) estimate. Statistic A measurement that describes part of a sample.

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Statistical significance The impact of a study in terms of the outcome of statistical tests conducted on the data. A study is said to be statistically significant when statistical tests demonstrate a difference between treatment groups. Statute Written law enacted by a legislature other than that of a municipality. Step therapy Prescribing guidelines set by the health plan in conjunction with the pharmacy and therapeutics committee that specify which drugs should be prescribed first before more expensive drugs will be covered. Guidelines are based on evidence-based medicine. Stratification An advanced type of randomization to produce study groups as similar as possible; considers baseline demographic information of the subjects selected for the study. Strict liability Also called absolute liability; the legal responsibility for damages, or injury, even if the person was not at fault or negligent. Strict liability has been applied to certain activities, such as holding employers absolutely liable for the torts of their employees, but it is most commonly associated with claims for injuries resulting from defectively manufactured or designed products. A successful plaintiff need only show that the product was in fact defective in design or manufacture, rendering it unreasonably dangerous and the cause of injury. Structure Refers to the characteristics of providers, the tools and resources at their disposal, and the physical or organizational settings in which they work. Structure indicators Quality assurance indicators based on the presence or absence of items, such as staffing patterns, available space, equipment, resources, or administrative organization. Study objective A brief statement of the goals and purpose of a research study. Subgroup analysis Evaluation of study results within a subset of subjects enrolled in the study according to specific demographic (e.g., age, gender, disease state). Subject An individual who participates in a clinical investigation (either as the recipient of the investigational drug or as a member of the control group). Summary judgment A party moving (applying) for summary judgment is attempting to avoid the time and expense of a trial when the outcome is obvious. A party may also move for summary judgment in order to eliminate the risk of losing at trial, and possibly avoid having to go through discovery (i.e., by moving at the outset of discovery), by demonstrating to the judge, via sworn statements and documentary evidence, that there are no material factual issues remaining to be tried. If there is nothing for the fact finder to decide, then the moving party asks rhetorically, why have a trial? A dispute over a material fact on which the outcome of a legal case may rely, and which, therefore, must be decided by a judge or jury; a dispute which precludes summary judgment. Surrogate endpoint A study measurement (e.g., laboratory value or physical assessment) that serves as a substitute marker for an actual clinical outcome. It is an effect that can be easily measured to correlate an outcome that is more difficult and/or time-consuming to measure (e.g., lowering LDL-C levels [measured effect] should result in reducing cardiovascular events [predicted outcome] such as myocardial infarction, stroke, or death). Survey research Research where responses to questions asked of subjects are analyzed to determine the incidence, distribution, and relationships of sociological and psychological variables. Switchability The ability to exchange one drug for another. Symposium A meeting focused on a particular topic.

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Synthesis Synthesis is the careful, systematic, and orderly process of integrating varied and diverse elements, ideas, or factors into a coherent response. This process relies not only on the type and quality of the data gathered, but also on how the data are organized, viewed, and evaluated. Synthesis, as it relates to pharmacotherapy, involves the careful integration of critical information about the patient, disease, and medication along with pertinent background information to arrive at a judgment or conclusion. Systematic review A summary of previously conducted studies where the research to be included in the review is systematically identified; however, the results are not statistically combined as would occur with a quantitative systematic review or meta-analysis. Also called a qualitative systematic review. Target drug program A program that evaluates the use of a medication or group of medications on an ongoing basis. Within these programs, interventions are usually made at the time of discovery based on established criteria or guidelines. Target population The entire group a researcher is interested in because this is the population that the findings of the survey are meant to generalize and the researcher wishes to make inferences and draw conclusions. Telemedicine It is defined as the use of telecommunications and interactive video technology to provide health care services to patients who are at a distance. Telnet A program for microcomputers that causes the computer to mimic a dumb terminal, so that it can run programs on other computers (usually minicomputers or mainframes) over the Internet or other computer networks. Ten major considerations A tool containing 10 items identified to evaluate a clinical trial article that if any single item is considered a limitation, the reliability of the entire article is questionable and the results deemed possibly unreliable. Teratogenicity Toxicity of drugs to the unborn fetus. Tertiary literature/resources Textbooks and drug compendia (includes full-text computer databases) that consists of established knowledge. The Joint Commission (TJC) Organization that accredits health care organizations and programs in the United States. Third-party payer Organization that pays for or underwrites coverage for health care expenses for another entity. Third-party plan A method of reimbursement for medical care in which neither the care provider nor the patient is charged. Third-party payers include insurance, health maintenance organizations, and government entities. Threshold A term used in quality assurance program that indicates how often an indicator must be complied with. Unlike standards, thresholds can be set at any level of compliance from 0% to 100%. Tiered copayment benefit A pharmacy benefit design that encourages patients to use generic and formulary drugs, by requiring the patient to pay progressively higher copayments for brand name and nonformulary drugs. Time trade-off A method for measuring health preferences. The subject is offered two alternatives. Alternative one is a certain disease state for a specific length of time t, the life expectancy for a person with the disease, then death. Alternative two is being healthy for time x, which is less than t. Time x is varied until the respondent is indifferent between the two alternatives. The proportion

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of the number of years of life a person is willing to give up (t − x) to have his or her remaining years (x) of life in a healthy state is used to assess his or her quality-adjusted life years (QALY) estimate. TJC See The Joint Commission. Tort liability Civil wrongs recognized by law as grounds for a lawsuit. Total quality management (TQM) A management concept dealing with the implementation of continuous quality improvement. TQM See Total quality management. Treatment effect A mean difference in the outcome measure over time between two drugs (i.e., drug minus placebo or test drug minus reference drug) Treatment order effect A situation where the order in which patients receive the treatment in a trial (generally a crossover design) affects the results of that trial. Trohoc study See Case-control study. True experiment A study where researchers apply a treatment and determine its effects on subjects. True negatives Individuals without the disease who were correctly identified as being disease free by the test. True positives Individuals with the disease who were correctly identified as diseased by the test. Two-tailed test A hypothesis that does not claim a direction of the difference or relationship. Type I error The probability of a false positive result. The probability of a type I error is equal to alpha and occurs when the null hypothesis is rejected when it is in fact true. Falsely rejecting the null hypothesis when, in fact, the null hypothesis is true. Type II error The probability of a false negative result. The probability of a type II error is equal to beta and occurs when the null hypothesis is accepted when it is in fact false. Failing to reject the null hypothesis when, in fact, the null hypothesis is false. Unexpected drug reaction The Food and Drug Administration defines this as “one that is not listed in the current labeling for the drug as having been reported or associated with the use of the drug. This includes an ADR that may be symptomatically or pathophysiologically related to an ADR listed in the labeling but may differ from the labeled ADR because of greater severity or specificity (e.g., abnormal liver function versus hepatic necrosis).” Uniform resource locator—URL An Internet address (e.g., http://druginfo.creighton.edu). Unsolicited requests Requests initiated by persons or entities that are completely independent of the relevant firm. USENET news A large number of discussion groups that are replicated in numerous places on the Internet. Users can read items posted on a topic and can contribute their own items to be posted. Validity The truthfulness of study results. Internal validity refers to the extent to which the study results reflect what actually happened in the study (i.e., appropriate and sound study methods). External validity is the degree to which the study results can be applied to patients routinely encountered in clinical practice. Validity filter A type of term or limit used to narrow a search to only the highest quality studies, such randomized controlled trial or double-blind.

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Validity, External See External validity. Validity, Internal See Internal validity. Value Has been assigned many definitions, but within health care, it usually reflects the ratio of quality and costs (value = quality/cost). Variables Factors (characteristics that are being observed or measured) that are the focus of a study. The independent variable (e.g., treatment) causes change in the dependent variable (e.g., outcome). Variance A measurement of the range of data values (i.e., variability) about the mean. Variance is the square of the standard deviation. Veracity This term addresses the obligation to truth telling or honesty. Vicarious liability Also called imputed liability or imputed negligence; the doctrine that attaches responsibility on one person for the failure of another, with whom the person has a special relationship (such as parent and child, employer and employee, husband and wife, or owner of vehicle and driver), to exercise such care as a reasonably prudent person would use under similar circumstances. Ordinarily, the independent negligence of one person is not imputable to another person. Virtual private network (VPN) A method to connect computers over a distance, for example, over the Internet, which allows secure transmission of confidential data. Warranty An assurance by one party to a contract of the existence of a fact upon which the other party may rely, intended to relieve the promisee of any duty to ascertain the fact for himself or herself. Amounts to a promise to indemnify the promisee for any loss if the fact warranted proves untrue. Warranties may be express (made overtly) or implied (by implication). Web 2.0 Simply refers to those applications of the Internet that are interactive and social, allowing for collaboration and interactivity among patients, caregivers, and providers. Web 2.0 is not new software but a different strategy to use the Web. The Web goes beyond being a search engine and a source of information to include a platform to create, share, and collaborate in developing new knowledge. Social networking is the phenomenon of online communities in which people share interests and/or activities with one another and is an outgrowth of Web 2.0. Web browser A computer program used to access information on the World Wide Web. The most popular programs are Microsoft Internet Explorer, Google Chrome, and Mozilla Firefox. Web portal A Web site that acts as an interface to the Internet for users. Many Internet search engines are considered to be Web portals. A variation on this, the enterprise portal, can also be used by an institution to help guide employees to necessary information within the institution or out on the Internet. Weblog (also known as blog) This is a public Web site where a person maintains a journal that is open to viewers. Web site A group of Web pages that will provide information to the person requesting that information. These pages are generally grouped under one main Internet address (URL). Wide area network (WAN) A group of computers connected in a way that they may share data, programs, and/or equipment over a distance (e.g., connection between computers owned by an institution that are scattered in clinics around a city). Wikis Web sites which allows its users to add, modify, or delete its content via a Web browser usually using a simplified markup language or a rich-text editor. Wikis are powered by wiki software,

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are created collaboratively, and can be community Web sites and intranets, for example. Some permit control over different functions (levels of access). For example, editing rights may permit changing, adding, or removing material. Others may permit access without enforcing access control. World Wide Web (WWW) Computers connected to the Internet that provide a graphical interface to a variety of information that is available as text, pictures, sounds, databases, and other electronic files. Generally accessed using a Web browser, such as Internet Explorer. XHTML—extensible HTML A combination of HTML and Extensible Markup Language. XML—Extensible Markup Language A superset of HTML that provides information on the content of a Web page, presentation of the information (how it looks), and semantics (what it means). This is designed to make it easier to find more relevant information using search engines.

z-score The distance a data point is from its variable’s mean in standard deviation units.

Answers for Case Studies Chapter 3 CASE STUDY 31 Common side effects would be included in all major compendia (e.g., Micromedex® 2.0, Clinical Pharmacology, or Facts & Comparisons) which would be a good initial search. In addition, some of the adverse effect specific resources (e.g., Meyler’s Side Effects of Drugs) would be appropriate to consult for less common side effects.

CASE STUDY 32 • There are a variety of resources that could be consulted for this information including the text

Drugs in Pregnancy and Lactation or some of the major compendia (possibly, Micromedex® 2.0 or Clinical Pharmacology). • The resources classify levofloxacin as an agent with unknown safety, but likely to be safe. • In order to best answer this question, the requestor should determine if the disease state has other treatment options which have more data available and if the infant is receiving any formula supplementation.

CASE STUDY 33 • The student might start a search for general information in a toxicology text such as Goldfrank’s

Toxicologic Emergencies. That could be followed with a search in Micromedex® 2.0 to find some general toxicology information; specifically the POISONDEX component of that resource would provide comprehensive information on this topic. • The student would do best to search using the generic name of the medication, in this case using chlorpheniramine.

CASE STUDY 34 In a case you are not familiar with a term, a general Internet search might be a good start to help streamline your search. An Internet search shows that AMDUCA stands for Animal Medicinal Drug Use Clarification Act of 1994. Knowing that the term refers to a specific piece of legislation, you would be prompted to consider searching the American Veterinarian Medical Association Web page or Food and Drug Administration Web page.

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CASE STUDY 35 • Since this would be an off-label use, there may be less data in the tertiary resources. In this case

it is likely more efficient to do a search in the secondary resources. Medline is a good place to start. • Initially conducting a search with no restrictions/limits ensures that valuable information is not missed. • If the initial search yields a significant number of results, then a restriction to human clinical trials would be beneficial. When conducting this search it is important to realize that the term female sexual arousal disorder has changed over time, so maybe a more general search for female sexual dysfunctions will give more responses. In addition, searching for the specific drug sildenafil will yield useful data, but expanding the search using the class of drugs will provide more data.

CASE STUDY 36 Since the patient specifically provided you the news venue, going directly to the NBC Web page would be an excellent start. Since content changes quickly on news pages, possibly a general Internet search might be needed if the story was run a while back.

CASE STUDY 37 • Tertiary resources could provide a good overview on how a treatment might work in a large

patient population, appropriate dosing, as well as provide a quick summary of safety data. The disadvantage is the lag time from when the information is updated until it is published, so recently discovered safety or efficacy information might not be included in that type of resource. • Primary literature would provide very timely information, but in a case like this the volume of primary literature can be overwhelming and make it difficult to navigate.

Chapter 4 CASE STUDY 41 1. Yes, any study enrolling human subjects requires IRB approval. This is especially true since this

clinical trial is evaluating an investigational agent and subjects may be at risk while partaking in this research. The IRB approval is needed to protect the enrolled subjects. 2. Placebo being selected as the control for this controlled clinical trial is appropriate. The purpose of this study is to measure and quantify the weight changes produced by lorcaserin. In addition, this study was designed to determine a cause-and-effect relationship with lorcaserin (cause = lorcaserin; effect = body weight changes). 3. This indicates that 68% (one standard deviation from the mean) of the body weight loss in the subjects of this trial treated with lorcaserin were measured to be between 3.4 and 8.2kg. 4. Since all subjects were to follow the same diet and exercise program, this would be classified as adjunctive therapy. The diet should not interfere with measuring a difference in weight changes between lorcaserin and placebo since both groups are following the same diet and exercise regimens.

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5. The probability of rejecting a true H0 is < 0.1%. Since this p-value is less than the stated α-value,

H0 would be rejected and H1 accepted. The probability of a Type I error is less than 0.1%. In addition, the probability of chance being the reason a difference was calculated between lorcaserin and placebo would be less than 0.1%. 6. Weight loss has been associated with lowering the risk of diabetes and cardiovascular disease. However, this study did not directly measure the number of subjects developing diabetes or reduction in the incidence of cardiovascular events (e.g., myocardial infarction). Lorcaserin did lower body weight, but these results cannot be used to claim this agent reduces the risk in developing diabetes or cardiovascular events. The primary endpoint results would be considered a surrogate endpoint for the reduction of diabetes and cardiovascular events.

CASE STUDY 42 1. Double-blinding is the most appropriate blinding type. The primary and secondary endpoints are

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subjective in nature. Thus, neither the subjects nor the investigators should know who were randomized to which group. Reduction in pain will, most likely, not be reported by individuals knowing they are taking placebo. In addition, the probability is highly likely that changes in pain scores reported by both subjects and investigators would be biased if the therapy is known. Ordinal. This type of data is classified as ranked or scaled. Subjects rated their pain during this trial using a scale. Rating pain on a scale is not dichotomous (e.g., yes/no data) and does not have equal intervals (as does continuous data). Mode and median are the appropriate measures of central tendency to present ordinal data. The mode is the most frequently occurring observation of the data set. The median is the point in which 50% (or middle) of the data in the set are above and 50% of the data are below. A power analysis is conducted by the investigators to determine a sample size for the trial. Power is the ability of the study to detect a difference between the intervention and control if a difference exists. An appropriate study power is at least 80%. This trial had a power of 90%. Study power increases with increasing sample size (and reduces the probability of a Type II error). A trial with 90% power requires a larger sample size than 80% power. According to the results of this study, duloxetine appears to be efficacious in reducing this pain type compared to placebo. However, other pain agents were not assessed in this clinical trial. Thus, the claim that duloxetine is more efficacious than other agents and duloxetine should be used as initial therapy cannot be supported with this controlled clinical trial.

CASE STUDY 43 1. RRR = 21%

RRR = 1 − RR. RR = (212/9120)/(265/9081) = 0.023/0.029 OR 2.3%/2.9% = 0.79 RRR = 1 − 0.79 = 21% The apixaban reduced the baseline risk of a stroke or systemic embolism by 21% over warfarin. This result indicates that apixaban reduces stroke or systemic embolism greater than adjusteddose warfarin. 2. ARR = 0.6% ARR = (265/9081) − (212/9120) = 0.029 − 0.023 OR 2.9% − 2.3% = 0.6% A total of 0.6% (or 55) patients were spared a stroke or systemic embolism by receiving apixaban versus adjusted-dose warfarin. This result indicates that apixaban reduces stroke or systemic embolism risk versus adjusted-dose warfarin. 3. NNT = 167 NNT = 1/ARR = 1/([265/9081] − [212/9120]) = 1/0.006 = 167

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A total of 167 patients need to be treated with apixaban for a median of 1.8 years to prevent one stroke or systemic embolism that would otherwise occur with adjusted-dose warfarin. 4. The hazard ratio (HR) for stroke or systemic embolism in this patient sample is 0.79 and the investigators are 95% confident that the true HR in the general population lies between 0.66 and 0.95. The value of equality (one) is not contained in this range, indicating the risk of stroke and systemic embolism is lower with apixaban versus adjusted-dose warfarin. Also, the result is statistical significance and potentially clinical significance. 5. This family member is an overall healthy male with minimal risk factors for stroke, other than his atrial fibrillation. He is well controlled on adjusted-dose warfarin, appears to be pleased with therapy, and is at low/moderate risk for bleeding adverse events. Unless he specifically desires to change therapy due to reasons other than those surrounding efficacy of warfarin (i.e., bleeding risk or adherence issues), continuing with his warfarin therapy is acceptable. Evaluating the primary endpoint result and all the measures of association calculations, the magnitude of difference between these two treatments may not warrant changing his stabilized and effective warfarin therapy in which no bleeding episodes have occurred.

Chapter 5 CASE STUDY 51 • Null hypothesis: The test drug (alginate/antacid) fails to exhibit noninferiority to the reference

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drug (omeprazole) or at the very least the results are inconclusive for noninferiority versus inferiority. Alternative hypothesis: The test drug (alginate/antacid) is noninferior to the reference drug (omeprazole). How the noninferiority (NI) margin was determined. Did the investigators set the NI margin prior to the study being conducted? Did the investigators confirm omeprazole’s efficacy against placebo which is referred to as assay sensitivity? Were historical trials and this NI study identical as possible regarding important characteristics (referred to as constancy assumption)? Yes, both intention-to-treat (ITT) and per-protocol (PP) analyses were used. Comparison between the two would have shown any concerns that the ITT approach diluted the results to show noninferiority. Smaller observed treatment effects can result with an ITT analysis since patients did not necessarily complete the duration of the trial and experience the maximum effect of alginate/ antacid. Using just an ITT analysis with an NI trial design can significantly increase the risk of falsely claiming noninferiority. The FDA recommends performing both ITT and PP analyses and checking to see if there are significant differences between the results. An explanation by the authors as to why there was a significant difference is expected. See Figure 5–1. For this noninferiority trial design, the conclusion would be that alginate/antacid is noninferior to omeprazole regarding short-term symptomatic efficacy in moderate GERD in a general practice setting. See Figure 5–1. For this noninferiority trial design, the result would be inconclusive regarding noninferiority for alginate/antacid compared to omeprazole since the 95% CI crosses over the NI margin instead of being located completely on one side noninferior or the other side inferior of the NI margin. Performing a superiority analysis after noninferiority has been established is acceptable and appropriate. It is generally not acceptable or appropriate to seek the conclusion of noninferiority from a failed superiority trial.

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CASE STUDY 52 • Null hypothesis: The test drug (linagliptin) fails to exhibit noninferiority to the reference drug

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(glimepiride) or at the very least the results are inconclusive for noninferiority versus inferiority. Alternative hypothesis: The test drug (linagliptin) is noninferior to the reference drug (glimepiride). In this case, there was no mention of NI margin. This situation dictates utilization of the p-value that resulted from the noninferiority statistical testing. The set alpha is 0.05. They reported a p-value of 0.03. The resulting p-value is less than the set alpha and, therefore, the null hypothesis is rejected in favor of the alternative hypothesis. For this study, the alternative hypothesis stated that the test drug (linagliptin) is noninferior to the reference drug (glimepiride). A modified intention-to-treat (mITT) analysis was used. Each patient that was placed in the evaluation group had received one dose of treatment, had a baseline HbA1c measurement, and had at least one on-treatment HbA1c measurement. The issue with mITT analyses involves the potential for smaller observed treatment effects since patients did not necessarily complete the duration of the trial and experience the maximum effect of linagliptin. This effect can dilute the results to show a false noninferiority between the two treatments. This is why the FDA recommends performing both ITT and per-protocol (PP) analyses to see if there are significant differences in results between the two analyses. An explanation from the authors as to why there was a significant difference is expected. A result showing a p-value greater than the set alpha would prevent us from rejecting the null hypothesis that states: failure to show noninferiority. Performing a superiority analysis after noninferiority has been established is acceptable and appropriate. It is generally not acceptable or appropriate to seek the conclusion of noninferiority from a failed superiority trial.

CASE STUDY 53 • Observation studies provide no guarantee that the two groups have similar characteristics with

the exception of being in the same observational cohort study. The women consuming four or more cups of coffee may be made up of healthier subjects than the group of women that consumed less than one cup of coffee per day. • Other factors (some not even known at this time) not controlled by this observational study may be playing an important protective role in those women that had an associated reduction in endometrial cancer risk. Potential confounding factors include health of immune system, genetic familial effects, and exposure to other factors that may be causing the cancer. There are many other confounding factors that can be identified. • The outcome should be measured the same way and at the same frequency for both groups. Adequate follow-up should be determined. • No cause-effect relationship can be determined with an observational study such as this cohort study.

CASE STUDY 54 • The quality of the meta-analysis depends on the quality of the individual studies used to develop

the meta-analysis in addition to the homogeneity of the studies as a whole. • Publication bias is a form of selection bias where publication of studies is based on the magnitude,

direction, or statistical significance of the results. It is documented that researchers are more likely to publish studies that demonstrate positive effects of drugs. The investigators utilized a technique called funnel plot to identify the potential existence of publication bias. A funnel plot is a scatter plot of treatment effect versus study size. If this plot shows an

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inverted symmetrical funnel, publication bias is probably not present. An asymmetrical funnel plot indicates the possibility that publication bias is present (see Figure 5–6). Factors that suggest a lack of high homogeneity or in other words high heterogeneity between studies: difference in primary outcome measure used, differences in participant attrition, difference in study design, differences in intervention duration, and wide variations in the vasoactive ingredient (polyphenol) between trials. The authors used a forest plot to provide the results of this meta-analysis because the reader can easily visualize the similarities and differences noted between studies and a confidence interval is included which provides additional information about the variability of the results for individual studies. Based on the findings reported here, although there is a statistically significant difference between chocolate and control, these findings are probably not clinically significant. Therefore, the authors should conclude that chocolate does not effectively reduce blood pressure in hypertensive patients. Proprietary content of active ingredients prevents you from being able to confirm whether any of the other products contain the same amount, or more or less than the product that provided this evidence. Also, specific plant parts utilized in a study are important to consider. If a trial evaluated the use of a herb’s root, but the product for which a practitioner is searching for information about contains the herb’s leaves and flowers, the results cannot be extrapolated. Unfortunately, not knowing the amount or exact part of the pelargonium plant used for the study prevents us from using this evidence for making evidence based clinical decisions for our patients. The majority of dietary supplement trials are conducted in Europe and Asia. Appropriateness of generalizability of results to a practitioner’s own patient population must always be considered, just as with standard drug trials. Probably not with this trial although it might take longer than 7 days to completely alleviate the symptoms of acute bronchitis so this would need to be taken into consideration. In this study, we saw clear clinical improvement in the patients receiving pelargonium. Small subject population is a common flaw with dietary supplement trials. This study utilized a reasonable number of patients to have some comfort with external validity. Also, the number of patients were adequate to meet power and show a difference between treatment groups. In addition, adverse reactions or drug interactions can be overlooked in smaller groups versus a larger one. This could be a reason why no adverse drug effects were noted.

Chapter 7 CASE STUDY 71 Establish transparency. Transparency should be planned from the beginning. Explicit details should be recorded throughout development. This information as well as funding information will need to be made publicly available. This first step is similar to selecting topics for a medication use evaluation program. There are specific disease conditions that possess the maximum potential for benefit from guideline development and implementation. These disease conditions share common characteristics such as high prevalence, high frequency/severity, availability of high-quality evidence supporting reduction in morbidity and mortality with treatment, feasibility of guideline implementation, potential cost-effectiveness, evidence that current practice is not optimal, evidence of practice variation, and the availability of personnel, expertise, and resources to implement a practice guideline if one is developed.

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Manage conflict of interest. Before the development group is selected, individuals being considered for membership will need to declare in writing all potential conflicts of interest. Conflicts of interest not only include financial conflicts of interest, but also intellectual conflicts which may occur as a result of previous research published by the individual, institutional conflicts of interest, and patient–public activities.

Establish a multidisciplinary guideline development group. Developing a clinical practice guideline should be considered a multidisciplinary process involving groups that have a stake in the development and implementation of the guideline. Patient involvement is especially important in helping to formulate and prioritize the questions to be addressed by the guideline. Anyone with expertise in guideline development would be valuable to the panel.

CASE STUDY 72 Conduct a systematic search for evidence. Several key steps are involved in conducting a systematic search for evidence. An appropriate topic for guideline development must be selected, the clinical questions to be addressed must be defined, and the study screening selection criteria should be determined before the systematic search is conducted. Once the systematic search for evidence has been done, individual studies should be appraised and then the body of evidence should be synthesized. Selecting a topic for guideline development is similar to selecting topics for a medication use evaluation program. There are specific disease conditions that possess the maximum potential for benefit from guideline development and implementation. These disease conditions share common characteristics such as high prevalence, high frequency/severity, availability of high-quality evidence supporting reduction in morbidity and mortality with treatment, feasibility of guideline implementation, potential cost-effectiveness, evidence that current practice is not optimal, evidence of practice variation, and the availability of personnel, expertise, and resources to implement a practice guideline if one is developed. Defining the clinical questions to be addressed is critical to be successful in searching for the necessary evidence and providing useful valid conclusions. Many guideline development groups use the PICO format for framing the question. The “P” stands for patients who are being considered for the question. Which treatment intervention to be considered is represented by the “I,” and the “C” stands for comparison or main alternatives that should be compared to the intervention. Finally, the “O” stands for what outcome is most important to the patient such as mortality, morbidity, treatment complications, rates of relapse, physical function, quality of life, and costs. The study screening selection criteria includes the types of published or unpublished research to be considered so that appropriate literature searches may be performed. The panel needs to decide if they will accept evidence from previous guidelines, meta-analyses, systematic reviews, randomized, controlled trials, observational studies, diagnostic studies, economic studies, and qualitative studies. This process may be revisited at various stages of guideline development depending on the results of the original search. Typically, a search is first conducted to identify previously completed guidelines and systematic reviews that involve related questions. The actual retrieval process should include a search of available bibliographic resources. Next a search of any specialized databases related to the subject of the guideline should be performed. Citations listed in published bibliographies, textbooks, and identified literature should be reviewed to identify other evidence not produced from database searches. Search terms can be identified from the clinical questions developed earlier by the panel. Several different methods exist for evaluating studies identified in the literature. The primary purpose of appraising individual studies is to identify issues with trial design or any potential biases that would affect internal or external validity. Some issues to consider include basic trial design, sample size, statistical power, selection bias, inclusion/exclusion criteria, choice of control group, randomization methods, comparability of groups, definition of exposure or intervention, definition of outcome measures, accuracy and appropriateness of outcome measures, attrition rates, data collection methods,

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methods of statistical analysis confounding variables, unique study population characteristics, and adequacy of blinding. Evidence represented by selected studies should be summarized in a format that allows the panel to begin developing conclusions. Best formats facilitate consideration of the characteristics and quality of individual studies, consistency of the results between studies, overall size of the evidence database, and size of treatment effects for benefits and harms.

CASE STUDY 73 Establish evidence foundations for and rating strength of recommendations. Recommendations in a guideline must be worded carefully and clearly communicate that the expected outcomes will be achieved if the recommendations are followed. These recommendations should be written to stand alone since users may not read the full guideline document. Confusion exists for the end user because a variety of grading systems are currently used by different guideline development groups. For each guideline that is used, practitioners are required to read the grading scheme description so they will correctly interpret the strength of the recommendations, quality of the evidence, and the balance between benefits and harms of the interventions considered. To minimize this confusion and potential for misinterpretation, a standardized system such as the Grades of Recommendation Assessment Development and Evaluation (GRADE) should be used by the panel to grade recommendations in the practice guideline being developed. A standardized form should be used to detail exactly what the recommendation is and when it should be done. Strong recommendations should be phrased so that compliance with the recommendation(s) can be evaluated.

Articulate recommendations.

Conduct an external review. Once a draft of the guideline has been created, it should undergo external review. All relevant stakeholders should be involved in the external review. Based on feedback from the external review, revisions may be required for the guideline to meet its intended goals. As with many steps in the guideline development process, one of the keys in this step is documentation. The decisions and actions taken in response to the recommendations from external review should be carefully documented. It is particularly important if there are critical recommendations that the guideline panel decides to reject that the reason for that decision is documented. At the time of external review or immediately following it prior to publication of the final guideline, a draft should be made available to the general public for comment. Establish a plan for updating the guideline. It is important that a plan for updating the guideline be established. The publication date and dates of systematic reviews used in the guideline should be clear. A plan to regularly review the literature to identify new technology or new evidence that may affect the guideline should be made, and a review interval to update the guideline should be established by the panel. The duration of the interval is dependent on the topic and knowledge of ongoing studies. A plan should also be made for an expiring guideline.

CASE STUDY 74 The seven categories of guideline implementation barriers that limit or restrict complete prescriber adherence include lack of awareness, lack of familiarization, lack of agreement, lack of self-efficacy (disbelief they could perform the behavior or activity recommended by the guideline), lack of outcome expectancy (disbelief that expected outcome would occur by following guideline), inertia of present practice (lack of motivation to change current practice), and a host of external barriers such as patient resistance, patient embarrassment, lack of reminder system, cost to patient, and lack of time.

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Chapter 8 CASE STUDY 81 1. 2. 3. 4. 5.

6. 7. 8. 9.

The population of interest is postmenopausal women. Random sampling was used from the convenience of the endocrinologist’s office. Yes. Patients were randomized to treatment group, and a placebo group was included. The DV is calcium absorption. It is measured on a continuous (ratio) scale. The IV is treatment group. The IV is ordinal in that the treatment groups can be rank ordered by dose size. The IV has four levels—placebo, 500 IU, 2500 IU, and 5000 IU. The size of the vitamin D dose is what is manipulated by the researcher. Yes, baseline calcium absorption and 25OHD measurements could be potential covariates. No, because the DV is continuous, the normal distribution should be attempted initially. No. Both variables are continuous, so they should be presented as mean, standard deviation. Data transformation is never wholly incorrect, so it could be done. However, because the data has severe positive skewness, a natural log transformation works better. With that said, a more appropriate option would be to use the gamma, inverse Gaussian, exponential, log-normal, Weibull, or Gompertz distributions, as described in Table 8–1.

CASE STUDY 82 1. No. Although patients are randomized to groups, there is no placebo or control group. That is,

both groups receive some form of treatment. 2. This study uses a parallel-group design with 1:1 randomization. Participants are randomly

assigned to one, and only one, treatment group. 3. No. The researchers did not specify that they were interested in EACA having less bleeding than

4. 5. 6. 7.

8.

TXA. They only stated they were interested in differences between EACA and TXA. This hypothesis could be answered with either more or less bleeding. Therefore, a two-tailed hypothesis would be most appropriate. Differences. Twice. Once at pre-op baseline and another 2 days post-op. No. The authors collected baseline hemoglobin at baseline they intended to use as a covariate. The only option is to test for group differences using a continuous covariate is ANCOVA. Yes. With a continuous DV, linear regression can handle any number of covariates measured on any scale. Because this study has covariates in addition to the IV, a multivariable linear regression is most appropriate. Yes. The patients are nested within doctors who are nested within hospitals. Forgetting about clustering can lead to inaccurate standard errors and bias in the statistical inference.

Chapter 9 CASE STUDY 91 • Since the topic is known and the boss informed you that you are the sole author, it is possible to

skip some of the first steps listed in this chapter. Also, it is known where it will be published—in

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the policy and procedure section of the institutional intranet. So, the first thing to do is probably review the format of policies and procedures in the institution, to determine what needs to written. As a part of this, determine that it needs to be written in the middle technical style, since it is being aimed at a variety of health care practitioners (e.g., pharmacist, physician, nurse). Then, it is necessary to do research into the topic. • First, organize the material. This can be done in conjunction with preparation of an outline of the order in which the material needs to be covered. That outline can done on a word processor and serve as the template for the document. In many cases, the way the institution lays out its policy and procedure documents can serve as a good part of the outline. Then proceed to write the material. At this stage, simply make sure everything necessary is recorded in the document. The document can be written in order of the topics, or each individual section may be written separately, in whatever order is easiest. Also, remember to cite material as the document is written. Besides being appropriate to give credit, it is also useful for the future when someone may have to come back to revise the document after several years and may not otherwise be able to tell the origin of some of the information. • Some would say to just present it to the pharmacy and therapeutics committee, but there are a couple of things that need to be done first. To start, the author should reread and edit the document. Then, get others who have expertise in the area to read and edit the document. These others should include one or more representatives from each group affected by the document (e.g., pharmacist, physician, nurse). An effort must be made to make sure the document is in a logical order, covers all aspects of the topic, and is understandable. Then incorporate any necessary revisions. This process may need to be done several times (e.g., some chapters in this book went through a dozen versions before being submitted to the publisher).

CASE STUDY 92 First, clarify who the Web site is addressing—the audience. It will be different for patients versus other health care practitioners. Sometimes it will be both groups. Then, in relationship to the above, determine what information or features need to be on the Web site. Then determine what equipment (e.g., computer hardware and software) and budget are available to prepare the Web site.

CASE STUDY 93 First, be sure to prepare slides on something that is compatible with the program used at the meeting. Then determine what information needs to be on the slides. Generally, assume that each slide should be shown for a minute or two. Each slide should be kept simple, with a maximum of five bullet points and five words per bullet point, so that attendees can concentrate on the message. Also, remember that the speaker is not to read directly from the slides, but use them just as a jumping off spot for the presentation and to organize thoughts.

Chapter 10 CASE STUDY 101 • Factors that favor finding the pharmacist liable for negligence: ° Pharmacist is a specialist (e.g., BCPS).

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° Anticoagulation clinical pharmacist. ° Reasonable pharmacist should know that an INR of 5.2 in a patient on warfarin places the

patient at increased bleeding risk. ° Reasonable pharmacist would question the use of the two medications together and document

the same. For example, enoxaparin may be used for bridging anticoagulation when initiating warfarin but the INR of 5.2 would not indicate warfarin initiation. • No. While combing these two drugs may increase the risk of serious or life-threatening bleeding complications, enoxaparin and warfarin may be used together to treat acute deep vein thrombosis with or without pulmonary embolism. However, where the pharmacist possesses special knowledge of the patient’s condition, there is a responsibility of the patient to clarify the order. ° If there is any protocol or guideline used at the clinic which was not followed. ° If there was no follow up or documentation of the rationale for the combo. • Yes. The courts would hold a specialist to a higher standard. The fact that the pharmacist is the anticoagulation clinic pharmacist and is board certified places this individual into a higher liability category. If there was a collaborative practice agreement it would be seen as a voluntary undertaking to provide expanded services to the physician and patient. • All three would be liable—the pharmacist, physician, and clinic under the theory of respondeat superior for actions of employee.

CASE STUDY 102 • The pharmacist fell below the standard of care. A health system pharmacist has access to the

patient’s renal function tests. Dispensing metformin without checking renal function falls below the standard. Not following a pharmacy department policy which requires both checking and documentation of the patient’s creatinine clearance prior to dispensing metformin also falls below the standard of care. Looking at the elements of negligence: (1) duty was breached; (2) damages resulted; (3) the damages would seem to be directly caused by the breach of the duty; and (4) defenses to not checking the renal function or following the policy are absent.

CASE STUDY 103 • Several activities occurring in this case violate the copyright law. ° Mere listing of all drugs which should not be crushed—derived from published references. ° Not classroom use. • Permission must be obtained to use material that is paraphrased, abridged, or condensed.

However, a new table created from data that is copyrighted sources would probably not require permission in this case. • Unless the reference was government materials, permission must be obtained. In looking at fair use, a four-pronged test is used—(1) nature and character of use, (2) nature of the work, (3) the proportional amount copied, and, most importantly, (4) the effect on the market for the copied work. • You take several direct sentences without providing source reference. One of the references is out of print. One factor in copying infringement is the amount copied. Even though only several sentences were copied, there is no minimal amount or threshold quantity standard where fair use would be presumed. The fact that some of the material is out of print does not mean the material is in the public domain. Out of print does not necessarily mean out of copyright. The rights revert to the author, and the underlying copyright remains unaffected.

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CASE STUDY 104 • HIPAA prohibits: ° The pharmacist sharing PHI and diagnosis in an area where it was easily overheard by

others. ° PHI faxed to a fax machine in an unsecured area where many people have access. • Safeguards: ° Private area ° FAX machine should be in a private area. ° Reasonable steps should be taken. Examples of such steps include: confirming with the

intended recipient that the receiving fax machine is located in a secure area or the intended recipient is waiting by the fax machine; pre-programming and testing fax numbers for frequent recipients of DI faxes to avoid errors associated with misdialing; double-checking the recipient’s fax number prior to transmission; using a fax cover sheet with an erroneous transmission statement and advising to notify the sender immediately and arrange for return or destruction of the fax; promptly checking all fax confirmation sheets to determine that faxed material was received at the intended fax number.

Chapter 11 CASE STUDY 111 • Assessment of whether this drug information request constitutes a potential ethical

dilemma: 1. What does it mean to you if confronting an ethical dilemma for such judgments of right or wrong to be ultimate/fundamental? 2. How do you interpret what it means for an ethical issue to be universal, in your own words? 3. Who are the various parties whose welfare could be impacted by the resolution to this situation, if indeed it does constitute an ethical dilemma? • Background information to obtain to clarify this information request: 1. What are facts of this case that you will want to learn more about before reaching a determination of whether it is indeed an ethical dilemma? (For example: What is the clinical condition of the patient in question? and, What standards or patient care commitments has your institution established for addressing patient pain issues?) 2. Considering who is affected is really a continuation of item #3 above defining an ethical dilemma. However, it may help you further to consider specific people involved in the case at hand—the patient himself; the supervising physician; the various other staff and trainees who must try to meet this patient’s needs under the circumstances at hand, and may learn to address future patients’ needs based on their experience; family members of the patient who will observe their loved ones’ suffering; the future patients who will be treated in similar or different ways based on the accumulating experience from this case. 3. What cultural perspectives might be at work for the prescribing physician (imagine for instance an older practitioner, or one trained in a particular perspective relative to standards of pain control). Likewise, what is the culture in the case environment relative to lines of authority, or freedom to question authority, or approaches to patient rights?

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• Given that a specific patient’s welfare must be addressed, this case certainly involves a micro level

of ethical decision making; however, as is often the case with ethical dilemmas, it might be argued that there are also meso level decision-making issues to be considered: What standards is the organization held to in meeting the pain control needs of its patients? What policies are there established within the organization regarding supervision/accountability of practitioners charged with patient care relative to specified standards ? • Refer to the listing of rules and principles provided in the chapter—Which if any seem to have relevance to this case? • Remember that rules often apply best to more narrow cases, and may be reasonably limited in some circumstances to meet the demands of more fundamental ethical principles. Decisions about competing principles will often rely on the decision makers’ priorities relative to primacy of anticipated good/bad consequences of the action (for the individual only? how about for society?) versus for instance certain core beliefs about fundamental rights (e.g., deontological principle prioritizing “respect for persons”). • What kinds of standards, policies, or procedures might be useful to this nurse specialist, both to aid in her ethical decision making and to provide support in her dealings with the prescriber and other involved staff as well as the patient/family? Refer to the article Ethical dilemmas: Controversies in pain management by Janet Brown to read the analysis of a similar case.1

REFERENCE 1. Brown J. Ethical dilemmas: controversies in pain management. Adv Nurse Pract. 1997:69-72.

CASE STUDY 112 • First recognize and understand the meaning of these characteristics as they apply to this specific

case—however, final determination of whether they apply will require the reader to first address other steps of analysis below. • There are a number of important factual questions that Dr. Rich is honor bound to address before deciding on any ethical dimensions of this case: 1. Examples: What is the drug in question? How available is it, other than relative to cost? Will use for the patient at hand impact availability for other patients with more clear-cut and pressing indications for use? 2. What is the level of evidence supporting or refuting the agent’s efficacy and safety when used for the requested purpose? - At a macro level of decision making, cost effectiveness is often deemed another appropriate factor to consider in recommendations for use at a population level. - However, policies on unlabeled use quite often also include “special circumstances” where a prescriber may be allowed to prescribe a specific therapy in a particular case with coverage provided, based on the available evidence supporting such use in similar cases. 3. What policies/procedures exist on appropriate action to address: when other alternatives are available to treat the medical condition, the seriousness of case brought up for “waiver” status, and general societal norms regarding prescriber/patient choice in desperate cases are other factors for meso or macro level decision makers to determine. • Certainly the professional must address this dilemma at a meso level. His or her decision making may be improved if he or she also thinks about what his or her responsibility is to the individual patients who will undoubtedly be impacted by the decision, as well as overall macro (system or societal) level consequences of policies established by this and similar organizations.

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• How do you think the principles of Respect for Autonomy or Fidelity might apply to this case? How

do you think Justice Theory might speak in favor of restrictions; for instance, in availability of scarce drugs for fully indicated therapies versus use for less-proven therapies? • Please imagine a specific product and case in order to personally decide how you would prioritize the various pertinent rules and principles in order to make a decision in such a case as this. Do you think that you would be justified in simply acting on the orders from your job supervisor, regardless of the factual circumstances and possible ethical issues of the case? • Organizational strategies that might best prepare this pharmacist to most effectively respond to ethical dilemmas: 1. What organization policies or standards do you think should be established in managed care

organizations to support ethical decision making pertinent to cases such as this one? 2. Do you think there are laws or regulations that should be in place at the governmental level to

guide managed care organizations in providing ethical service to their insured populations? If so, what measures do you think could be useful?

Chapter 12 CASE STUDY 121 • See Appendix 12–6 for an example of routine agenda items. Review the minutes from the previous

meeting. Add agenda items based on the meeting minutes such as follow-up information or new agenda items that came from discussion. Verify agenda with Director of Pharmacy and P&T Committee Chair. • You would need to know some background on the formulary application, who requested it and for what application. You would need to be familiar with similar agents within category (if applicable) for comparable indications, dosing, cost, etc. It is not unreasonable to have several communications with the person requesting the new medication or other specialists who may have insight into formulary justification. The summary page should include the following: generic name, trade name, indications, clinical pharmacology, pharmacokinetics, adverse reactions, drug interactions, dosing, product availability and storage, drug safety/REMS, pricing, conclusion, and references. • Intranet communication, e-mails, newsletters, personal communication, dissemination through appropriate meetings.

CASE STUDY 122 • Nonformulary request form • All proton pump inhibitors (esomeprazole, lansoprazole, dexlansoprazole, omeprazole, panto-

prazole, rabeprazole) are generally considered therapeutically equivalent. While there are many examples of published interchange protocols (both inpatient and outpatient), there may be a given patient who may have a better tolerability and response for one product over another. • Most inpatient hospitals have preprinted orders for preapproved therapeutic interchanges. This form has been approved by the P&T committee. The pharmacist has been granted the responsibility to automatically fill the order with the equivalent dose of pantoprazole. Once the form is

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completed it becomes part of the medical record (electronic or paper). If an interchange is chosen for a medication that is not on the preapproved therapeutic interchange list, then a new order must be written for the alternative agent. • You can tell the physician the process for filling out the formulary request form.

CASE STUDY 123 • Physicians, pharmacists, nurses, students, and administrators affiliated with the health

system. • Newsletters, announcements at meetings, intranet communication, e-mail messages, personal

communication, or even posting alerts in specific areas of the hospital. • Current inventory, expected length of shortage, alternative agents available. • In the case of furosemide, other loop diuretics are available alternatives. You may find it necessary

to restrict the remaining inventory to specific hospital units or for a specific diagnosis. You may emphasize that during the shortage oral furosemide should be used whenever possible. It would be wise to produce a comparative chart for equivalent dosing and route of administration for alternative loop diuretics (bumetanide, furosemide, or torsemide). This comparison chart can be sent along with the message regarding the shortage.

Chapter 13 CASE STUDY 131 • Steps to add this drug to the formulary: ° Review nonformulary utilization and indications for use. ° Review utilization of similar agents in the therapeutic class (if applicable) that are on the formulary. ° Seek input from the appropriate specialists who would have knowledge of the product or

recommendations for formulary status. ° Review available literature to evaluate clinical evidence. ° Consider discussions with pharmacists at other hospitals. ° Determine financial implications of product addition by reviewing cost information with

purchasing agent and contract information from the wholesaler and manufacturer. ° Prepare a drug monograph. • Essential elements of a medication monograph: ° Generic name (Trade name) ° Approval rating ° Therapeutic class ° Sound/Look-alike ° Indications/place in therapy ° Adverse effects ° Drug interactions ° Recommended monitoring ° Dosing ° Product availability and storage

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Drug safety/REMS Comparative pricing information/pharmacoeconomic analysis Formulary implications/Conclusion/Recommendation References • Sources of information to develop a complete, evidence-based, medication monograph: ° Current published clinical studies and abstracts ° Nonpublished data or data awaiting publication (contact manufacturer) ° Current package labeling ° Obtaining a formulary kit from the manufacturer may be helpful for double-checking information, but this should not be the primary reference source for your monograph ° Check current evidence-based clinical guidelines ° ° ° °

CASE STUDY 132 • Each of the following would be necessary, with comparison to other similar products. Essentially,

it will be a quick comparison of the major similarities and differences, which can be fit on a single page. ° Generic name and trade name ° Indications ° Clinical pharmacology ° Pharmacokinetics ° Adverse reactions ° Drug interactions ° Dosing ° Product availability and storage ° Drug safety/REMS ° Evidence-based clinical guidelines ° Recommendation • The following information should be included: ° All of the previous mentioned items ° Defined tier status for copayments ° Restrictions—such as prior approvals ° REMS • Different types of formulary status recommendations: ° Added for uncontrolled use by the entire medical staff. ° Added for monitored use—No restrictions placed on use, but the drug will be monitored via a

quality assurance study (e.g., drug usage evaluation and medication usage evaluation) to determine appropriateness of use. This is a tie-in to the institution’s quality assurance/drug usage evaluation process. Note: This category does not mean that the patient is monitored, since that is necessary for every drug. It means that the quality and appropriateness of how the drug is used are monitored. ° Added with restrictions—The drug is added to the drug formulary, but there are restrictions on who may prescribe it and/or how it may be used (e.g., specific indications, certain physicians or physician groups, and certain policies to be followed). ° Conditional—Available for use by the entire medical staff for a finite period of time. ° Not added/deleted from formulary. The product can be ordered as a nonformulary product, but will not be routinely stocked in the pharmacy. Nonformulary products may take up to 24 hours or longer to obtain.

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Chapter 14 CASE STUDY 141 Possible responses include: • Staff do not know that the medication should be infused over 60 minutes. • The medication label does not provide infusion-time directions to the nurse administering the medication. • Nurses are not using the smart features of the infusion pumps that would default to the appropriate infusion rate for a 60-minute infusion. • Staff are rushed and opt to infuse medications more rapidly in order to get everything done.

CASE STUDY 142 Possible responses include: • Physicians (surgeons, medicine/family medicine, infectious disease specialists) • Nurses from the perioperative areas, inpatient units, and home care areas • Pharmacists • Discharge planners/case managers who help plan for discharge and arrange home care

CASE STUDY 143 Existing guidelines or standards for warfarin use to assist in drafting criteria for MUE can be found using any of the following: • Search the medical literature. • Ask practitioners with experience/expertise in this area. • Professional organizations (pharmacy, nursing, medical). • Identify any prior evaluations of this topic with the organization. • Post an inquiry on professional listservs.

Chapter 15 CASE STUDY 151 • There was no dechallenge in this case; the patient has been taking the product continuously for

about 6 weeks. • Although there was not a formal dechallenge and rechallenge, the patient reports taking the prod-

uct again after her symptoms abate. Her symptoms reappear each time. • Yes. The patient’s symptoms are always preceded by administration of the drug, and they disap-

pear soon afterward. • Yes, the patient’s symptoms are consistent with the known pharmacology of ZygoControl Weight

Loss. The product contains synephrine, which has stimulant effects. The pharmacology of synephrine is similar to its isomer, phenylephrine. • Using clinical judgment, you can determine that the product is likely responsible for the side effects described. This is supported by the fact that the symptoms occurred after ingestion of the

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product, the symptoms recurred following rechallenge, and the symptoms are consistent with the known pharmacology of the ingredients contained in the product. You could also complete one of the algorithms discussed above in order to assess the likelihood that this reaction was caused by ZygoControl Weight Loss.

CASE STUDY 152 • There are several options for reporting the reaction to the FDA. You can report this ADR to the

MedWatch program by phone, or by filling out Form 3500 and submitting it through mail, fax, or the Internet. Because this ADR involved a nonprescription natural supplement product, you also have the option of reporting the reaction through a third-party system such as Natural Medicines Watch, or directly to the product’s manufacturer or distributor. These entities will forward your ADR report to the FDA. • You patient can report the ADR either by completing MedWatch Form 3500B, or contacting one of the FDA’s Consumer Complaint Coordinators.

Chapter 16 CASE STUDY 161 • Advantages and disadvantages of including multiple cases in the root cause analysis process: ° Combining cases of similar types provides a larger volume of information from which to

extract potential causes common to many events. For example, reviewing one case and making major system changes may in fact miss the most common factor that is causing the events. The power of multiple cases increases the chance that the frequent causative reasons are identified, which can then be addressed. ° One disadvantage is that each case that is included in an aggregate root cause analysis (RCA) requires adequate investigation in order to understand the causes and, therefore, takes more time. All the cases are then reviewed in aggregate to determine those most common causes.

CASE STUDY 162 • Questions you would like to ask the pharmacist involved in the error as part of an interview: ° It is important to ask staff to provide a description of the situation as they remember it, without

leading questions. It is also very important to interview staff early to prevent unintentional alterations to the story based on fading memory or hearing other discussions related to the event. ° Ask the pharmacist to describe what happened and what they remember. ° Ask the pharmacist what they do if/when they realize an error has occurred. What resources are they aware of to identify what patient they may have entered the orders on in error—many computer systems have a searching function that enables a report that identifies all patients on a given drug? ° Ask other pharmacists what their process is when interrupted to ensure they are entering orders on the correct patient.

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• Classification of error as human-error only, system-error only or combination: • It depends on the information that is gathered during interviews and observation of the usual

process. If there exists the opportunity to devise a no-interruption zone for order entry (in which telephone calls are received by other staff), then this is a system opportunity. It is a human error in that it was not an intentional action and the system set up the pharmacist to potentially fail. It is likely considered a combination of both human error and system contributing cause.

CASE STUDY 163 • System issues that contributed to the error: ° The nurse had more patients than the recommended nurse:patient ratio. ° The drug label had the volume first and the drug listed second, yet the infusion pump requires

programming the drug first and the volume second, setting up the nurse to program it incorrectly. • Your reaction as manager of the unit: ° Were the actions as intended? Did the nurse intentionally program the pump incorrectly? No. ° Was the person under the influence of unauthorized substances? No. ° Did he or she knowingly violate a safe operating procedure? There was not a required double

check and pump programming was taught to all staff upon institution of the new pumps. She did not make a conscious choice to skip steps within the process or subvert the process. ° Do they pass the substitution test described above? When this was discussed with several other nurses, two of the three noted that they had made a similar error and/or caught a similar error. This appears to be a skill-based error in which the nurse has done it many times and this time there was a slip. Would others have made the same decisions and, if so, less likely to be culpable? If not, were there deficiencies in training or experience? The training must be carefully considered. Providing didactic information without practice or competency testing is not the most appropriate method of teaching staff. Adequate practice is needed to develop good habits and skill-based actions. ° Does the individual have a history of unsafe acts? This is the first error of this type that has been identified for this nurse. If not, again less likely to be culpable. • Identifying potential system fixes: ° The label design should match the entry in the pump if at all possible. Rearranging or increasing the visibility of the required information for programming is another option. ° Providing adequate staffing ratios to decrease the risk of competing priorities with multiple patients is appropriate. • What other system fixes can you think of?

CASE STUDY 164 • Identifying system or process issues: ° The epidural medication was brought into the patient’s room before there was an order, at the

request of the anesthesiologists. They wanted to have everything ready when the patient and team decided it was time for the epidural. This increases the risk of inadvertent administration due to availability. ° The nurse had not placed an identification band on the patient, which is required for use of the medication bar-code scanning process. Part of this was because of the lack of immediate availability of labels in the patient’s room or on admission, requiring the nurse to go searching for the identification band. A contributing factor was found to be a prior tolerance of not using the bar-code scanning on this unit. ° The nurse did not use the bar-code scanning technology to verify the medication prior to administration. This would have detected the error prior to administration to the patient if used correctly. This was partly due to the lack of patient identification band and a unit tolerance

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to inconsistent use of this technology. This unit experienced a difficult implementation with this process that was ineffective at times, leading nurses to skip the process. ° The nurse picked up the wrong medication, didn’t closely read the label, and prepared the incorrect medication. Contributing factors to this human error include the similar bag size and look of the two medications, a rushed nature, and a low suspicion of risk—never having experienced an error or problem in the treatment of a laboring mother in this manner. This nurse was fatigued due to her work schedule and distracted, both of which increase the risk of a human error. Read the related article cited below to identify additional information about the case. Lead a dis• cussion related to system errors and their impact on health care professional’s behaviors and the potential impact to patients. Smetzer J, Baker C, Byrne FD, Cohen MR. Shaping systems for better behavioral choices: lessons learned from a fatal medication error. Jt Comm J Qual Patient Saf. 2010 Apr;36(4):152-63. a. Discuss challenges with implementation of new technology. b. Discuss challenges in culture and prioritization of safety principles. c. Discuss confirmation bias and look-alike packaging.

Chapter 17 CASE STUDY 171 • Yes, the product is going to be used in a different patient population and be infused using a differ-

ent route of administration. In both cases the risk profile is higher for the new usage and as such submission of a new IND is going to be required. The company may select to work with Dr. Smith’s data to further develop the product, develop an appropriate protocol, and submit the IND to the FDA. Alternatively, the company may select to simply support Dr. Smith as he develops a protocol and IND for submission. In that case the company will provide Dr. Smith with a letter of cross reference for his use in supporting his IND application.

CASE STUDY 172 • A well-researched comparison of the risks of liver metastases as compared to the risks of cirrhosis

will be needed to justify further development of this product using this route of administration. In addition, submission of an REMS will be crucial. • Appropriate components of the REMS: ° Letters to health care providers ° Patient medication guide ° Patient registry to track enrollment of patients receiving the drug via this route of administration ° Patient monitoring of liver function tests

CASE STUDY 173 • The IRB is likely to determine that not only must this risk be included in the consent form, but that

if the purpose of the new studies is to obtain further information about this risk, that must be explained to the subjects as part of the consent form.

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• Children are considered a special population in clinical research. 45CFR46 specifies criteria for the

evaluation of risk as compared to benefit when research is conducted in children. In this case it is likely that an IRB would still consider this study to be approvable since the product holds the promise of potential benefit to the patient in addition to the known risks. In children prior to the age of legal majority (as defined by state law), their guardians are responsible for making health care decisions for them. In some situations the agreement of one guardian is sufficient; however, if the IRB has determined that there is risk to the child and no direct benefit to them, the agreement of both guardians is required. In addition, the agreement of the child (referred to as assent) is also required. • The point at which a child reaches the age of majority they must provide their own consent to participate in the study.

Chapter 18 CASE STUDY 181 • Steps used to approach this assignment: ° Collect information on the background related to this formulary decision. ° If unfamiliar with the role and development of an automatic therapeutic interchange, engage in

background readings to help understand this type of policy, including the strengths and limitations. ° Determine what the standard format/template will be for policies. ° Conduct a systematic search to determine comparative dosing, safety profiles, and cost con-

siderations. ° Review the information gathered in your search and consider this information in the context

of the needs of the health system. ° Solicit input from colleagues in similar institutions or professional organizations for sample

policies. ° Prepare a draft policy that is specific, yet succinct, and well referenced. ° An automatic therapeutic interchange should include specific guidance on how nonformulary

agents will be converted to the formulary agents and should include all potential medications, all usual prescribed regimens (i.e., drug, dose, route, frequency) with an equally specific regimen for the formulary agent. ° Convene a group of stakeholders and solicit input on the policy. • A variety of resources can be used in this process. As discussed in Chapter 3, an appropriate search should begin with tertiary references and should progress to secondary resources and eventually to primary literature in this scenario. The safety and efficacy of the HMG-CoA reductase inhibitors is well documented in the tertiary literature and resources such as Micromedex®, AHFS® Drug Information, and textbooks (e.g., Pharmacotherapy Principles and Practice) are a good starting point for understanding a comparison of the agents with respect to clinical efficacy, safety profile, and dosing and administration considerations, in addition to other parameters. Following a thorough search of the tertiary literature, in this case, it is appropriate to conduct a literature search in a secondary database (e.g., PubMed®) to identify primary literature that supports a conversion from one agent to another. Adequate clinical trials should be collected and evaluated to assess the appropriateness of such a therapeutic interchange policy. Ideally, comparative head-to-head studies would be available to draw conclusion about comparative efficacy and safety. Finally, cost should be obtained directly from the pharmacy department regarding institution-specific pricing to develop a cost comparison for the conversion to the formulary agent.

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• Once the information is collected from tertiary and primary resources, a thorough evaluation

should be conducted. This will involve a critical assessment of the literature in the context of the needs of the organization. • Stakeholder and identify key stakeholders for this policy are defined as follows: ° A stakeholder is an individual who has a vested interest in the matter and policy in question. ° For the policy on an HMG-CoA reductase inhibitor automatic therapeutic interchange, the key stakeholders would be physicians in the specialties of cardiology, internal medicine, and family medicine. This policy would affect the pharmacy department and clinical pharmacists practicing in these specialties as well. ° Once a draft policy is developed, it should be presented to key stakeholders for review and input prior to its presentation for approval by a pharmacy and therapeutics committee or medical director. This can be accomplished in several ways. Perhaps a formal meeting of the stakeholders could be convened (e.g., expert panel) or individual discussions and dissemination of the draft could be handled by the pharmacist responsible for drafting the policy.

Chapter 19 CASE STUDY 191 • Drug information questions that TH has either requested or implied: ° What is meant by the term “not on formulary?” ° What is covered on this patient’s prescription formulary? ° What other options (pharmacologic [Rx and OTC] or nonpharmacologic are there besides

what the patient has determined to be an expensive prescription drug? • Sue might begin to answer each of these questions as follows: ° “Not on formulary” means that a medication is not listed in a prescription drug plan as being

paid for nor provided at a discounted rate through a prescription insurance plan. ° Prescription formulary coverage information may be determined in a variety of ways, includ-

ing visiting http://medicare.gov for Medicare recipients, typing prescription insurance provider + formulary + the calendar year you desire (e.g., 2014) in an Internet search engine, searching select drug information databases such as Epocrates® or Lexicomp®, or within e-prescribing platform formulary decision support systems. ° Alternative treatments for heartburn may be researched within current treatment guidelines. Accurate, reputable treatment guidelines may be quickly found in sources such as National Guidelines Clearinghouse (NCG), the Iowa Drug Information Service, PubMed®, as well as through links from the Web sites of the American Society of Health-System Pharmacists (ASHP) at http://www.ashp.org/bestpractices or from the American Pharmacists Association™. Reputable databases with information geared specifically toward the patient, and with materials • that have been reviewed and placed at eighth-grade reading level or below include Clinical Pharmacology, Facts and Comparisons Online, Lexicomp, and Micromedex. • Clinical Pharmacology®, Facts and Comparisons Online®, and Lexicomp®, each contain patient-oriented materials in both English and Spanish. In addition, the Lexicomp® database includes medication leaflets in up to 19 additional languages, and Micromedex®’s Patient Connect Suite includes medication information in up to 15 languages geared toward the patient (although the reading level for the additional languages in LexiComp® and Micromedex® is not specified).

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CASE STUDY 192 • In general, the most important thing to advise the consumer is to avoid flushing medication and

avoid pouring them down the drain (according to the SMARxT DISPOALTM Campaign). Consumers should be encouraged to take advantage of medication take-back collection days. If none are available, methods for safe personal disposal of medications can be found on the Institute for Safe Medical Practice Web site (http://www.ismp.org) as well as the Pharmacist’s Letter Web site (http://www.pharmacistsletter.com). A very small number of drugs may be flushed (due to the potential risk of inappropriate exposure), and these are listed online at http://www.fda.gov/downloads/ Drugs/ResourcesForYou/Consumers/BuyingUsingMedicineSafely/EnsuringSafeUseofMedicine/ SafeDisposalofMedicines/UCM337803.pdf as well as in Table 19–1 of this chapter. • While multiple governmental initiatives exist to promote the safe and appropriate disposal of unused, unwanted, and expired medications (e.g., The White House Office of National Drug Control Policy and that of the U.S. Fish and Wildlife Service’s SMARxT DISPOSAL campaign), these are not enforceable laws on the consumer.

CASE STUDY 193 • Examples of such quality indicators include, but are not limited to, efficiency (resource use),

structure, process, intermediate outcomes, long-term outcomes, and patient centeredness. Newer measures may include those focused on medication-related patient safety (e.g., detecting/preventing medication errors and adverse drug reactions). • Yes it is, and it falls under the quality measure related to patient centeredness. • Yes; The U.S. Department of Health and Human Services, as part of the Accountable Care Organization model has specified and published 33 required quality measure that are evaluated in order to determine payment structure for patient care networks (including ambulatory care settings).

Chapter 20 CASE STUDY 201 • As a pharmacist in a busy pharmacy, it is important to triage the problems in front of you. In this

situation, you have multiple things going on and you are the only pharmacist on duty. It is up to you to decide what takes priority. This patient may be making decisions about her health based solely on information found online. If the patient follows through on her plan to stop taking the antidepressant, she will be at risk of being harmed. This should make her your top priority. The patient in this particular case is actually seeking input from you in her attempt to have a dialogue about quitting her antidepressant. You would be negligent in your duties if you did not choose to counsel the patient on the pros and cons of obtaining health information online, danger of stopping an antidepressant abruptly, and risk of recurrence of her depression. As a pharmacist, you have a professional obligation to engage with your patients and provide them with the education and tools to obtain the best health care possible. Patients are increasingly using alternative sources beyond health care providers for advice and/or counseling, which makes it vital that pharmacists initiate even difficult conversations. • First, the patient should be encouraged to continue to take an active role in her own health care. Patient empowerment has many positives for health outcomes. On the flipside, it is important to

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make the patient aware that when taking her health care into her own hands there can be negative consequences as well. It is crucial that she involve a health care practitioner if she decides to change her therapy in anyway. Second, agree with your patient that there are a lot of good places to find health information out there but that it is really important they identify quality Web sites to obtain that information. Even then, every individual is different and the information they find on these Web sites may not necessarily be patient specific or applicable to his or her situation. For example, the patient in this case is making a decision to discontinue her antidepressant based on other patients’ opinions of the medication. These other patients quite possibly have an entirely different health situation. Encourage the patient to take these concerns to her primary care practitioner so that they may have a discussion on whether or not what she found online is relevant to her situation. Seems like it is missing a space between the period and the T in third. Third, you review suggestions for determining a quality health information Web site, thereby ensuring she is obtaining information from reputable sources and bringing information to discuss with her health care practitioner that has value. In this specific case, you are extremely busy and unable to meet now so you should arrange a time to call the patient or make an appointment for her to come back in to discuss her plans. Our role is to support the patient and their beliefs. Show respect and the desire to collaborate with them and you will gain their trust. Trivialize what they bring to you and insist that the health care practitioner is the expert and the one who knows best and you may damage the relationship beyond repair. • Safety first. If the patient has made up her mind that there is no stopping her from discontinuing her antidepressant immediately, advising her how to do so safely becomes your number one priority. First, direct the patient to see her primary care practitioner as soon as possible. Together they can work out a plan of action to taper her off the medication and possibly get her started on something she feels more comfortable taking. The practitioner may also establish a plan in order to monitor the patient for signs of relapse into depression. If you sense the patient will not consult her physician, it is important to counsel her on possible withdrawal symptoms she may experience as well as the consequences of quitting her medication all at once without tapering. Also arrange a follow up time after your initial discussion to evaluate the control of her depression and how well she tolerated stopping the medication. At follow-up, if the patient is suicidal or at imminent risk of harming herself or someone else it is imperative you seek immediate assistance.

CASE STUDY 202 • The first question that should come to mind as a pharmacist is whether or not the patient has

discussed the use of this particular app with his physician. Although the tracking of health data to assist in the management of a disease state can be extremely helpful for the patient, it can become dangerous when an app is making clinical recommendations about treating a condition without the supervision of a health care professional. Second, it is important to confirm the patient is not currently in a health crisis. Although the patient sings the app’s praises because of the positive difference he feels in terms of his disease state since downloading it, you want to verify that his blood glucose levels truly are well controlled. Depending on the situation, some disease states may be easier than others to evaluate at the pharmacy. Most of the time the patient will need to be redirected back to their physician in order to be sure that the app has been helping not hurting. At that time, it’s a perfect opportunity for the patient and physician to discuss the mobile software and whether or not it will be a part of their treatment plan going forward. Finally, you should ask more questions about the particular app in question. Was it developed by a credible source? Are clinical recommendations based on evidence? Is the app capable of tailoring a recommendation to a specific patient? The answers to these questions and others may help you better guide the patient in whether the app is a reliable one or in some cases FDA approved as a medical device to use in managing a disease state.

ANSWERS FOR CASE STUDIES

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• In this particular scenario, it seems the patient has not just dipped his toes into the mobile health

arena but has jumped head first. The app he describes using puts him, the patient, in a position to be very reliant on a device to make therapeutic decisions his physician would normally make. While it seems the application does factor in patient-specific data when making its decisions, without the supervision of a clinician this is risky territory. The single best piece of advice you can give a patient who is interested or has been using mobile health apps is to always check with a health care professional before making any changes to their prescribed medication regimen. Use of mobile health software by a patient wanting to get more involved in their health should never be discouraged; in fact, the benefits seen in patients who use apps to keep track of health and fitness progress, find health information, and stay connected with others in their health situation are infinite. The danger lies when patients start using apps as a sole resource to diagnose, manage their disease state, adjust their medication regimens, etc. Smartphone applications with these types of capabilities should be used in conjunction with a physician. In some cases, they may be medically prescribed.

CASE STUDY 203 In general, there are five key areas that are important to consider when evaluating a new mobile health application: credibility, accuracy, whether or not it is evidence based, ease-of-use, and health literacy. There are several questions under each key area that are important to ask yourself as a reviewer of an app. • Credibility of application Are credentials of the app suitable? Are authors/publishers clearly listed? Are there advertisements? Is the organization that developed the app reputable? Are there disclaimers of content? • Accuracy of information Is it peer reviewed? Is the information current and/or frequently updated? Are recent and reputable guidelines used to support recommendations made? Are references cited? • Evidence-based medicine Are recommendations evidence based? Do recommendations target a specific audience or are they general in nature? Are opinion statements clearly marked? Are users directed to a health care professional before making changes to health care routine? • Ease-of-use Does the app fit to the screen? Is the setup of the app well designed and organized? Is the app easily navigated? Does the app have a search function? Is there a main menu that helps clearly lay content out? • Health literacy Is medical jargon used easy for the lay reader to understand? Is font and setup of app easy-to-read? Does app gear information for the consumer? Unfortunately, even after thoroughly reviewing a mobile health app using the five key areas there still may be some questions as to whether or not it is an app that is capable of making therapeutic decisions for a patient. In these instances, it may be best to suggest contacting the developer of the software to better determine how their particular app arrives at therapeutic decisions.

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Chapter 21 CASE STUDY 211 Answers for Case Studies 1. d 2. d

Chapter 22 CASE STUDY 221 1. Awesome Drugs R Us, Inc. should prepare standard operating procedures (SOPs) to make their

employees aware of the regulations governing their job functions. 2. Awesome Drugs R Us, Inc. should include, at a minimum, Title 21 of the Code of Federal Regula-

tions and pertinent Food and Drug Administration (FDA) Guidance documents in education materials on external regulations for their staff. 3. The medical affairs department at Awesome Drugs R Us, Inc. is responsible for all company sponsored clinical trial programs related to investigation Drug A®. The medical affairs staff are also responsible for corresponding with field staff and key opinion leaders in addition to reviewing promotional and marketing materials in anticipate of launch of Drug A®. If approved, medical affairs will be responsible for reviewing Drug A® product labeling and marketing dossiers. 4. Other departments Awesome Drugs R Us, Inc. should consider developing include medical information, medical science liaisons, and field based outcomes liaisons. Responsibilities of each include: Medical Information: ° responds to inquiries, provides training, and drafts materials for internal and external

audiences ° reviews promotional materials to ensure fair-balance of risk and benefit information ° develops portions of formulary dossier ° responds to escalated medical information requests, some may be off-label requests

Medical science liaisons (MSLs): ° ° ° °

supports scientific affairs and medical information staff out in the field maintains close relationships with key opinion leaders engage in clinical conversations cover a large geographic area while supporting a single product

Field-based outcomes liaisons (FBOLs): ° demonstrates the value of Drug A® to managed care organizations, pharmacy benefit

managers and others in decision making or purchasing positions

° creates tools to help clinicians evaluate the value of Drug A® such as a risk stratification tool

ANSWERS FOR CASE STUDIES

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CASE STUDY 222 • Medical education is the department which would review grants for education programming for

Drug A®. • Medical information and medical affairs will be involved in approving marketing materials for Drug A®. They will work closely with legal and regulatory staff.

CASE STUDY 223 • Awesome Drugs R Us, Inc. should draft a standard response letter discussing the similarities and

differences between Drug A® and STRIPES™ in terms of mechanism of action, side effects, and other characteristics, in anticipation of inquiries regarding product comparisons to STRIPES™ and other investigational compounds. • Awesome Drugs R Us, Inc. should anticipate questions on yellow stripes and anything related to that adverse event (AE) based to their similar indication to STRIPES™.

CASE STUDY 224 • Awesome Drugs R Us, Inc. will document patient reports of yellow stripes on Form FDA 3500A. • Awesome Drugs R Us, Inc. is not required to share reports of yellow stripes with any additional

agency or organization. Awesome Drugs R Us, Inc. has chosen to voluntarily inform the Institute for Safe Medication Practices and has alerted a patient advocacy group for those suffering from mild to moderate hiccups of the increased incidence of yellow stripes and how to report the adverse effect.

CASE STUDY 225 • FDA communicates safety information to patients in many ways beyond updating the prescribing

information (package insert). Some of the ways FDA communicates safety information includes: ° Drug Safety Communications ° Medication Guides ° Patient Package Inserts ° Communication Plans ° Elements to Assure Safe Use

CASE STUDY 226 • The manufacturer of SPOTS™ should consider voluntarily submitted a proposed Risk Evaluation

and Mitigation Strategy (REMS) with their New Drug Application based on the known risks associated with Drug A® and STRIPES®.

CASE STUDY 227 • Pharmacists cannot dispense single ingredient albuterol CFC MDIs after December 31, 2008.

Information to support this answer is found on FDA’s Web site (www.fda.gov) by searching for “albuterol CFC phase out”. One helpful resource is titled “Making the Switch: Prepare your patients for the phase-out of CFC-propelled albuterol inhalers”. This article was also featured in the November 2008 issue of Pharmacy Today, the official publication of the American Pharmacists Association.

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• Yes, the pharmacist may automatically switch patients from an albuterol CFC to and HFA inhaler.

Information to support this answer may be found in the online article mentioned above. The article states, “Any of the three HFA propelled products containing the active moiety albuterol (ProAir HFA Inhalation Aerosol, Proventil HFA Inhalation Aerosol, and Ventolin HFA Inhalation Aerosol) are acceptable replacements for CFC propelled products containing albuterol, even though they are not generically equivalent to CFC-propelled products.” • Counseling points are provided in the article mentioned above regarding the differences patients may notice when switching from CFC to HFA albuterol inhalers. The patient should be counseled using this information, which states, “Notably, the force of the spray of an HFA-propelled inhaler may feel softer than that of a CFC-propelled inhaler. Patients should be reassured of the drug’s effectiveness, even though the spray may taste different or not feel as strong as that from a CFC inhaler.” • The FDA also provides a response to pricing concerns in the article mentioned above. Regarding pricing, the FDA notes, “Because no generic products are available, patients may also have concerns about the higher cost of HFA propelled albuterol inhalers. Some drug companies have patient assistance programs that make medicines available to patients at no cost or at a lower cost. In addition, some patients may be able to get help paying for medicines from CMS [Centers for Medicare & Medicaid Services].” The FDA does not have statutory authority to investigate or control the prices charged for marketed drugs. Prices are established by manufacturers, distributors, and retailers. FDA does not stipulate what drugs insurance companies may cover or to what extent the drug may be covered. The Federal Trade Commission (FTC) enforces a variety of federal antitrust and consumer protection laws. The FTC accepts complaints on their Web site on the prices of marketed drugs. Please ponder the last part of this question; there is no right or wrong answer. • Evaluating both sides of an argument helps develop empathy and understanding for opposite points of view. Express your concern for the mother’s fears. Listen carefully for what help she needs. Share appropriate information about the Montreal Protocol, as required by the Clean Air Act, and the United States agreement as a signatory country to follow its terms, including the elimination of the use of CFCs.

Chapter 23 CASE STUDY 231 • A help-seeking advertisement should include a list of possible symptoms for a particular disease

and appropriate images of individuals who may be experiencing the discussed symptoms. The advertisement should encourage patients to discuss their symptoms and seek medical advice from their physicians. In addition, the advertisement should provide company information and references to a telephone number or Web site for more information. • The help-seeking advertisement should not include images or references to drug products to treat listed symptoms. • Additional evaluation of the advertisement can be completed using the PhRMA Guiding Principles. Accuracy of the disease state information being presented should be assessed. In addition, the seriousness and respectfulness of the disease state should be maintained throughout the advertisement. Help-seeking advertisements that may be inappropriate for children should not be presented in mediums where children have access to the advertisement.

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CASE STUDY 232 • It is very important to have a clear conflict of interest policy in place. This policy and procedure

should guide who may meet with the pharmaceutical industry, how often individuals should meet with industry representatives, what topics are discussed, and what types of information and/or items may be exchanged. It is also wise to have a system in place to track and document these visits. Finally, it is also beneficial to complete a training session or course regarding best practices for industry interactions. • Prior to the visit, it is recommended to determine what medications will be discussed, either by directly asking the representative or by reviewing their portfolio. Key evidence-based information from resources such as the prescription drug labeling will be used. It is also helpful to conduct a primary literature search and ensure you are up-to-date with current clinical literature regarding the medications. • Be polite and professional but also use active listening skills to detect use flawed logic (e.g., appeal to authority, red herring).

CASE STUDY 233 • Health care professionals should critically analyze all information presented at these programs for

accuracy, reliability, and potential violations. • Common violations likely to occur in this setting include inadequate risk information, overexag-

geration of benefits, presenting off-label or unapproved information, making false or deceiving comparisons with other medications. • Health care professionals can report advertising violations to the FDA through the Bad Ad program via email: [email protected] or by phone: 855-RX-BADAD (855-792-2323).

CASE STUDY 234 • Some type of needs assessment should be done prior to launching an academic detailing program-

ing. Possible sources of data include review of prescribing practices, patient demographics, financial data, and survey of health care professionals and/or patients. • Materials used in academic detailing should be developed using an evidence-based approach; they should draw from tertiary resources and primarily literature. One organization that may provide validated materials is NaRCAD. Academic institutions may also serve as valuable partners. • The advantage of a one-on-one approach is that it may facilitate a more meaningful discussion as well as allow for privacy of the prescriber. However, a group approach has the advantage of potentially reaching a larger audience. Either approach could be considered depending on the scope and goals of the program. • Metrics should be specific to the intervention targets and actionable. For example, if a program was designed to promote evidence-based use of antihypertensives, prescribing patterns could be monitored with the goal of observing increased adherence to an institutional guideline. If increasing use of generic medications was a goal, prescription data could also be monitored to ensure effectiveness. Finally, humanistic data may also be collected in the form of prescriber and/or patient survey.

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Chapter 24 CASE STUDY 241 • Commonly encountered technologies used: ° Prescribing—computerized provider order entry, clinical decision support systems, electronic

prescribing ° Transcribing—clinical decision support systems ° Dispensing—automated dispensing cabinets, carousel cabinets, robotic cart filling systems,

sterile compounding devices ° Administration—bar code medication administration systems, electronic medication adminis-

tration records, intelligent infusion pumps ° Monitoring—clinical surveillance systems, clinical documentation systems

CASE STUDY 242 • Journal of Medical Internet Research, Journal of Participatory Medicine. • Initially, Web 2.0 tools, such as Facebook, can be used to provide general information about

services offered, showcase the expertise of staff, and provide basic health and medical information. As the institution’s comfort with the technology grows, Facebook can be used for online question and answer sessions, to host videos of highly advanced procedures, and to provide patient perspectives on their experiences at the facility. Twitter can also be used to send the latest news about significant additions to the medical staff, inform followers about upcoming clinical education classes, and highlight the acquisition of high tech tools for patient care. As resources become available and as patients express interest, Twitter may also be used for targeted messaging to remind patients of activities that encourage healthy behaviors. Ultimately, the information that is shared on Facebook, Twitter, or any other medium should address the needs of the institution’s patients while maintaining privacy and confidentiality. The best way to find out what information patients want to receive or access electronically is to ask them.

CASE STUDY 243 • What group or organization is overseeing the EHR Meaningful Use program?

– Centers for Medicare & Medicaid Services (CMS) • Why would a hospital or provider want to start Meaningful Use earlier rather than later?

•

• • •

– To receive the monetary incentives offered by CMS and because implementation is expected to result in better clinical outcomes, improved population health, increased health care transparency and efficiency, more robust data for research, and the empowerment of individuals and patients. What are the stages of Meaningful Use? – Answer is fine as is (i.e., There are three stages of Meaningful Use, focused on (1) capturing patient data and sharing that data with either the patient or other health care providers, (2) advanced clinical processes, and (3) improved patient outcomes.) The Office of the National Coordinator for Health Information Technology The Centers for Medicare and Medicaid Services There are three stages of Meaningful Use, focused on (1) capturing patient data and sharing that data with either the patient or other health care providers, (2) advanced clinical processes, and (3) improved patient outcomes.

Answers for Self-Assessment Questions Chapter 1 1. c

6. e

11. a

2. e

7. a

12. e

3. b

8. b

13. e

4. c

9. d

14. a

5. e

10. c

15. a

1. a

6. c

11. d

2. c

7. d

12. d

3. c

8. c

13. b

4. a

9. e

14. b

5. b

10. e

15. e

1. e

6. b

11. a

2. b

7. a

12. e

3. a

8. d

13. b

4. d

9. c

14. d

5. c

10. e

15. c

Chapter 3

Chapter 4

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Chapter 5 1. c

6. d

11. b

2. a

7. c

12. a

3. b

8. c

13. d

4. d

9. c

14. a

5. c

10. b

15. d

1. d

6. d

11. b

2. c

7. a

12. a

3. c

8. d

13. b

4. c

9. a

14. a

5. b

10. c

15. c

1. e

6. c

11. e

2. c

7. b

12. e

3. d

8. d

13. c

4. d

9. b

14. a

5. e

10. e

15. b

1. a

6. e

11. b

2. e

7. c

12. d

3. a

8. b

13. a

4. c

9. b

14. e

5. d

10. a

15. a

Chapter 6

Chapter 7

Chapter 8

ANSWERS FOR SELFASSESSMENT QUESTIONS

Chapter 9 1. e

6. b

11. b

2. b

7. b

12. b

3. b

8. a

13. b

4. e

9. a

14. d

5. b

10. a

15. b

1. a

6. b

11. c

2. e

7. e

12. d

3. b

8. e

13. b

4. b

9. a

14. e

5. d

10. e

15. e

Chapter 10

Chapter 11 1. a, c, and d

6. b, c, and d

11. b

2. a

7. a

12. d

3. a, b, c, d (There could be 8. a relevance of any of these 9. b, c, and d rules/principles.) 4. b 10. a, b, and d

13. c 14. d 15. a, b, c, and d

5. d

Chapter 12 1. a

6. d

11. d

2. a

7. d

12. d

3. d

8. a

13. b

4. c

9. a

14. a

5. a

10. a

15. a

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Chapter 13 1. a

6. d

11. d

2. a

7. e

12. a

3. b

8. b

13. a

4. a

9. a

14. a

5. a

10. d

15. a

1. d

6. e

11. c

2. d

7. a

12. c

3. a

8. e

13. e

4. d

9. c

14. e

5. e

10. d

15. e

1. d

6. a

11. e

2. c

7. d

12. c

3. c

8. c

13. e

4. d

9. a

14. e

5. b

10. e

15. a

1. b

6. b

11. a

2. c

7. a

12. e

3. c

8. c

13. b

4. d

9. e

14. a

5. c

10. b

15. c

Chapter 14

Chapter 15

Chapter 16

ANSWERS FOR SELFASSESSMENT QUESTIONS

Chapter 17 1. a

6. c

11. d

2. b

7. b

12. b

3. c

8. d

13. e

4. a

9. a

14. d

5. c

10. c

15. c

1. c

6. b

11. c

2. d

7. e

12. b

3. a

8. b

13. a

4. d

9. e

14. e

5. a

10. e

15. c

1. b

6. b

11. b

2. c

7. d

12. c

3. d

8. b

13. b

4. b

9. e

14. d

5. d

10. a

15. a

1. e

6. e

11. d

2. e

7. b

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3. d

8. e

13. c

4. e

9. d

14. e

5. e

10. b

15. d

Chapter 18

Chapter 19

Chapter 20

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Chapter 21 1. d

6. e

11. d

2. d

7. d

12. c

3. c

8. d

13. d

4. a

9. d

14. e

5. d

10. d

15. a

1. b

6. d

11. c

2. a

7. a

12. b

3. a

8. d

13. a

4. c

9. b

14. b

5. d

10. c

15. d

1. e

6. d

11. b

2. a

7. b

12. d

3. b

8. a

13. c

4. a

9. d

14. b

5. c

10. a

15. a

1. b

6. d

11. a

2. e

7. d

12. b

3. c

8. e

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4. c

9. d

14. a

5. b

10. e

15. e

Chapter 22

Chapter 23

Chapter 24

Index A A priori NI margin, 196 protocol based clinical trials, 377 AAASPS. See African American Antiplatelet Stroke Prevention Study AACP. See American Association of Colleges of Pharmacy AAPCC. See American Association of Poison Control Centers Abbreviations in professional writing projects, 468 P&T committees and, 646 ABC, 92t Absolute difference in the effect (δ), 141-142, 153 Absolute risk reduction (ARR), 155-159, 157t Abstracting services BIOSIS Previews, 64t, 85 CANCERLIT, 86 CINAHL, 86, 324, 876 Cochrane Library, 86, 244, 324, 341 Current Contents Connect, 63t, 86, 324 described, 60, 83, 108t EMBASE, 63t, 64t, 86, 108t, 237, 254, 324, 508, 910 Google Scholar, 87, 90, 531, 532, 533 International Pharmaceutical Abstracts, 63t, 64t, 87, 108t, 114, 237, 876 Iowa Drug Information System, 63t, 64t, 83, 87, 237, 877, 909 Journal Watch, 87, 167 LexisNexis, 63t, 64t, 88, 91, 108t MEDLINE, 5, 63t, 64t, 86, 88, 108t, 237, 254, 324, 341, 349, 508, 532, 543, 910 Pharmacoeconomics & Outcomes News Weekly, 88 PubMed, 11, 88, 108t, 114, 166, 237, 254, 508, 532, 876, 909, 910 Reactions Weekly, 63t, 64t, 88, 751 Abstracts controlled clinical trials, 110t, 114-115 meeting abstracts, 112, 474t, 514 for presentations, 485 structured, 114, 167, 341

Abuse, on Internet, 535-537 Academia, medication information specialists in, 19, 23-24 Academic detailing drug promotions and, 1031-1035, 1032t to implement clinical guidelines, 336 Academy of Managed Care Pharmacy (AMCP) AMCP format for formulary submissions, 975 drug evaluation monographs, 671, 683, 691 P&T committees, 609 therapeutic interchange, 636 Access Pharmacy electronic subscription, 74, 76, 77 AccessPharmacy, 82 ACCME. See American Council for Continuing Medical Education Accountable Care Organizations (ACOs), 921, 928 ACCP. See American College of Chest Physicians; American College of Clinical Pharmacy Accreditation Council for Pharmacy Education (ACPE), 24, 551, 955, 957, 958, 959 Accreditation Manual for Hospitals (AMH), 720 ACE inhibitors. See Angiotensin-converting enzyme inhibitors Acknowledgments, in clinical trials, 110t, 163 ACOs. See Accountable Care Organizations ACP. See American College of Physicians ACP Journal Club, 167 ACPE. See Accreditation Council for Pharmacy Education Acquired immunodeficiency syndrome (AIDS), 468, 473, 527, 581, 832. See also Human immunodeficiency virus Acronyms for medication use evaluation, 719-720, 720t in professional writing projects, 468 for quality improvement, 706 Act step, PDCA Cycle, 706-707 Action guides. See Ethics Active control, 121, 139, 191-192, 197, 832 Active therapy (standard therapy), 109, 123, 141, 839

1269

1270

INDEX

ACTs. See Adaptive clinical trials Acute care pharmacy setting, pharmacy informatics, 1056-1057 Acute Stroke Therapy by Inhibition of Neutrophils (ASTIN) trial, 204-205, 208 Ad hoc committees, P&T committees, 620-622 ADA. See American Diabetes Association Adaptive clinical trials (ACTs), 203-208. See also Controlled clinical trials; Study designs analyzing, 384 ASTIN study, 204-205, 208 benefits, 204, 208 biases, 205, 206t-207t, 208 purpose, 190t RCTs compared to, 377, 378-379 ADCs. See Automatic dispensing cabinets Added-with-restrictions, recommendation for, 679-680 ADEs. See Adverse drug events ADHD. See Attention deficit hyperactivity disorder Adherence attrition and nonadherence, 142, 143, 144, 145, 378, 379, 380 controlled clinical trials, 131-132, 143-145 Adis International Pharmacoeconomics & Outcomes News Weekly, 88 publications for specific therapeutic areas, 89 Reactions Weekly, 63t, 64t, 88, 751 Adjunctive therapies, 148 Administering, in medication use process, 719t Administration, pharmacy informatics, 1058 Admissible evidence, 323-324 Admission bias, 206f, 222 ADRs. See Adverse drug reactions Advanced pharmacy practice experiences (APPEs), 18, 24, 955, 958-959, 964 Adverse drug events (ADEs). See also Adverse drug reactions; Medication and patient safety; Medication errors; Medication misadventures ADEs-ADRs-medication errors-medication misadventures relation, 742, 742f, 779-782, 781f defined, 779-782 HIT tool, 1048 HR-QOL measurements and, 251 identification of, 784-787 medication information provision and, 9-10 MEDMARX, 756, 787, 793 organizations involved in preventing ADEs, 744t, 783t pharmaceutical industry and, 984-985 postmarketing surveillance, 9-10, 234 questions in defining, 782t reporting of, 784-787, 792-793 resources on, 63t, 67 risk factors, 806-809 safety event analysis, 795-797

self-assessment questions, 821-824 suggested readings, 827-828 Trigger Tool, 785 triggers for, 794t Adverse drug reactions (ADRs), 741-776. See also Adverse drug events; Medication and patient safety; Medication errors; Medication misadventures ADEs-ADRs-medication errors-medication misadventures relation, 742, 742f, 779-782, 781f ADR program implementation, 752-758 Bayesian approach, 749, 765 case studies, 749-750, 764 causality and probability of, 745-751 cause and effect in, 745, 751 classification of, 751-752 conclusion, 766-767 dechallenge, 41t, 746, 747-748, 749 definitions, 742, 744-745, 779-782 dietary supplement ADR reporting, 761-763 genome-wide association studies and, 766 impact of, 743-744 indicator drugs, 754t introduction to, 742-745 MEDMARX, 756, 787, 793 MedWatch program, 750, 756, 757, 759-761, 762, 764, 769 Meyler’s Side Effects of Drugs, 63t, 67, 750 pharmacovigilance, 743, 750, 759, 764-766, 776, 962 postmarketing surveillance, 758-759 probability and causality of, 745-751 P&T committees and, 645 rechallenge, 41t, 746, 747-748, 750 reporting, 758-764, 792-793 safety event analysis, 795-797 self-assessment questions, 767-770, 821-824 Side Effects of Drugs Annual, 63t, 67, 750 specialty resources, 750-751 suggested readings, 776 technology for, 755-758 terminology confusion, 742 vaccine ADR reporting, 764 Adverse Reaction Tracking, 757 Adverse reactions committee, 620 Advertising. See also Direct-to-consumer advertising; Drug promotions HPs and, 977 legal aspects of doctrine of drug overpromotion, 526-527 DTC drug information, 524-526 erosion of learned intermediary rule, 504, 506, 510, 523, 524-526 off-label use and informed consent, 527-530 Advisory committees, FDA, 689, 845 Affordable Care Act, 13, 274, 302 African American Antiplatelet Stroke Prevention Study (AAASPS), 148

INDEX

Agency for Healthcare Research and Quality (AHRQ) ADE prevention, 783t American Pharmacists Association Web site and, 922 on clinical practice guideline development, 341, 342 Common Formats, 788 e-prescribing platforms with FDS, 908 MEADERS, 756 NaRCAD, 1033-1034, 1035 National Guideline Clearinghouse, 908 patient and medication safety best practices, 819t policy development and, 873 purpose, 987t quality of health information checklists, 944 Agency for Toxic Substances and Disease (ATSDR), 987t Aggregators, 638-639 AGREE. See Appraisal of Guidelines for Research & Evaluation AGREE II. See Appraisal of Guidelines for Research & Evaluation II AHA. See American Heart Association. AHA/ACC evidence-based guidelines for cardiovascular disease prevention in women, 322 AHFS. See American Hospital Formulary Service AHFS Drug Information reference book, 65, 673-674 AHRQ. See Agency for Healthcare Research and Quality AIDS. See Acquired immunodeficiency syndrome Alert fatigue, 757, 1051, 1054 Alginate, 198 Algorithms ADR causality, 746, 747-749 power algorithm, 258, 258f Alpha (α) value, 138, 151f, 195, 387-388 Alternative drug information resources, 90-93, 92t Alternative hypothesis. See Research hypothesis Alternative medicine. See Complementary and alternative medicine AMA. See American Medical Association Ambidirectional cohort study, 216 Ambulatory care/ambulatory care clinicians. See Drug information in ambulatory care AMCP. See Academy of Managed Care Pharmacy AMDUCA. See Animal Medicinal Drug Use Clarification Act American Academy of Pediatrics, 75 American Association of Colleges of Pharmacy (AACP) ACCP Drug Information PRN survey, 958 Argus Commission, 946

1271

Basic Resources for Pharmacy Education listing, 62 educational outcomes maintained by, 8 Ethics Course Content Committee of, 570 information therapy, 946 journal recommendations of, 89 therapeutic interchange, 636 American Association of Poison Control Centers (AAPCC), 22 American College of Chest Physicians (ACCP), 112, 319, 323, 326, 620 American College of Clinical Pharmacy (ACCP) Directory of Residencies and Fellowships, 963 Drug Information Practice and Research Network, 18, 958, 961 electronic listservs, 877 leadership conferences and seminars, 892 PGY1 and PGY2 residency programs, 960 on programmatic research, 212-213 therapeutic interchange, 636 American College of Physicians (ACP), 167, 636 American Council for Continuing Medical Education (ACCME), 504, 551-552 American Diabetes Association (ADA), 909, 916 American Drug Index, 63t, 68 American Druggist, 68 American Heart Association (AHA), 163, 909 American Hospital Formulary Service (AHFS). See also Formularies drug classification, 64t, 65, 66, 94t, 529, 530, 673-674 Drug Information reference book, 65, 673-674 American Medical Association (AMA) American Medical Association Manual of Style, 465, 480 clinical decision support, defined, 1053 continuing education recommendations, 1028 CPT codes, 1062-1063 e-mail guidelines, 533 Journal of the American Medical Association, 109, 464, 744 NCCMERP founding member, 780, 780t P&T committees guidelines, 609 American Medical Informatics Association, e-mail guidelines of, 533 American Pharmacists Association (APhA) Handbook of Nonprescription Drugs: An Interactive Approach to Self-Care, 63t, 66 NCCMERP founding member, 780, 780t Patient Safety and Quality Assurance page, 922 pharmacist.com, 66, 69, 909, 920, 922 Pharmacy-Based Immunization Delivery, 920 practice guidelines, 909 Project Destiny, 931, 947 SMARxT DISPOSAL campaign, 917 therapeutic interchange, 636 Trissel’s Stability of Compounded Formulations, 63t, 69

1272

INDEX

American Recovery and Reinvestment Act, 758 American Society for the Prevention of Cruelty to Animals (ASPCA), 80 American Society of Health-System Pharmacists (ASHP) ADR monitoring program, 753-755 adverse drug events, prevention, 744t, 783t AHFS Drug Information reference book, 65, 673-674 ASHP-accredited PGY2 residency programs, 956, 961-962, 970 Best Practice policies and treatment guidelines, 909 classification of medication errors, 787 clinical practice guidelines, 876 counterfeit drug products, 647 Directory of Residencies and Fellowships, 963 drug evaluation monograph recommendations, 671-672, 678-679 drug information specialists, position papers and standards, 505 Extemporaneous Formulations (Children’s Hospital of Philadelphia), 68 generic substitution, 637 Handbook of Injectable Drugs, 63t, 72 Health Summit Working Group, 532 industry-sponsored continuing education, education content, 1028 leadership conferences and seminars, 892 levels of evidence, 249f, 257, 686t, 688t, 877f listservs, 877 medication errors reporting program, 793 risk factors, 806-807 medication misadventure, definition, 779 MUE guidelines, 721 NCCMERP founding member, 780, 780t Online Residency Directory, 963 patient and medication safety, best practices, 819t Patient Information section and, 691 Patient Monitoring Guidelines section and, 691 The Pharmacist’s Guide to Evidence-Based Medicine for Clinical Decision Making, 263, 272 pharmacovigilance, 743 policy development with, 873 power algorithm, 258, 258f product shortages case study, 649-650 guideline, 649 P&T committees, 609, 611, 641 risk factors for errors and events, 806-807 SafeMedication.com, 94, 94t social media statement, 535, 568 specialty practice residencies in medication information accredited by, 18-19

technology and, 10-11 therapeutic interchange, 636 American Veterinary Medical Association (AVMA), 79-80, 81 AMH. See Accreditation Manual for Hospitals Aminoglycosides, 279, 512, 874 Amitriptyline-TCA study, 420-422 Amlodipine, 51, 52, 53, 126 Analysis and synthesis, 44, 46, 50, 53, 56. See also Drug information queries Analysis of covariance (ANCOVA), 138, 381, 384, 395t, 398, 412-414, 435 Analysis of variance (ANOVA) controlled clinical trials, 138 factorial between-groups, 395t, 410-412, 420, 421, 422 Friedman two-way ANOVA by ranks, 395t, 424 Kruskal-Wallis one-way ANOVA by ranks, 395t, 415-416 mixed between-within, 381, 383, 420-423 one-way between-groups, 384, 394, 395t, 408-410, 411, 412, 415, 416, 432 one-way repeated measures, 418-420, 421, 422, 423, 424 Ancillary therapies, 148 ANCOVA. See Analysis of covariance AND, Boolean operator, 83-84, 84f Angie’s List, 935t Angina pectoris, 123 Angiotensin-converting enzyme (ACE) inhibitors, 46, 47, 155-156, 161, 665, 683, 754t, 755 Animal Medicinal Drug Use Clarification Act (AMDUCA), 79, 81-82 Animal Poison Control Center, 9, 80 Annals of Pharmacotherapy, 112 ANOVA. See Analysis of variance Antacids, 198, 483, 635, 636 Antibiotic Use Review (AUR), 719, 720t, 729 Anticancer agents, 14 Anticoagulation ad hoc P&T committee for, 620 apixaban (Eliquis), 159-160 dabigatran, 556, 683-684 dosing policies, 640 enoxaparin, 130, 295, 507 therapy safety, 813, 875, 889, 889f warfarin, 159-160, 295, 507, 644, 645, 683-684, 726, 887, 888f, 910, 921 Antidiarrheal agents, 754t, 794t Antihypertensive therapies, 51, 52, 53, 133, 153, 161, 194, 196, 248, 257 Anti-Infectives Today, 89 Antikickback statute, 535-536, 553-554, 557 Antimicrobials, ad hoc P&T committee for, 620-621 Antithrombotic Therapy and Prevention of Thrombosis, 112, 319

INDEX

APhA. See American Pharmacists Association Apixaban (Eliquis), 159-160 APPEs. See Advanced pharmacy practice experiences Applied Biopharmaceuticals and Pharmacokinetics, 64t, 74 Applied Therapeutics: The Clinical Use of Drugs, 64t, 76, 875 Appraisal of Guidelines for Research & Evaluation (AGREE), 312, 334 Appraisal of Guidelines for Research & Evaluation II (AGREE II), 312, 334-336, 335t. See also Evidence-based clinical practice guidelines Argus Commission, 946 Aripiprazole, 131 ARR. See Absolute risk reduction Arthroplasty, total joint, 399-400 Article proposal, 461, 461f ASHP. See American Society of Health-System Pharmacists ASPCA. See American Society for the Prevention of Cruelty to Animals Aspirin, 120, 148, 166, 239, 509, 510, 718 Assessment, SBAR technique, 816 Associated Press, 92t Association of American Publishers v. New York University, 541 Association tests nonparametric tests logistic regression, 381, 384, 391, 396t, 437-441, 442, 446, 448, 454 Spearman rank-order correlation coefficient, 396t, 428, 436-437 survival analysis, 441-450 parametric tests multiple linear regression, 396t, 399, 412, 428, 433-436, 437, 440, 446 Pearson’s product-moment correlation, 396t, 428-430, 429f, 431, 432, 434, 436, 437, 441, 450 simple linear regression, 394, 396f, 397, 430-433, 434, 435 ASTIN trial. See Acute Stroke Therapy by Inhibition of Neutrophils Atenolol, 412, 413 Atomoxetine, 68 Atorvastatin (Lipitor), 117-118, 124, 131, 139, 146, 153, 158, 893, 1015 Atrial fibrillation, 159, 160, 323, 683 Atropine, 754t, 794t ATSDR. See Agency for Toxic Substances and Disease Attention deficit hyperactivity disorder (ADHD), 68, 83, 84, 90 Attributable risk, 215 Attrition, nonadherence and, 142, 143, 144, 145, 378, 379, 380

1273

Audience. See also Professional writing of newsletters and Web sites, 478 of presentations, 487 of professional writing projects, 464, 466t, 467 AUR. See Antibiotic Use Review Authors of professional writing projects, 462-463 on published articles, 111-113 Autism, Childhood Autism Spectrum Test, 415 Automatic dispensing cabinets (ADCs), 1048, 1056 Autonomy principle, 574, 575, 578-579, 580, 581, 585, 586f Availability, of dosage forms, 63t, 68 Avandia. See Rosiglitazone Average. See Mean Average wholesale price (AWP), 691-692 AVMA. See American Veterinary Medical Association AWP. See Average wholesale price

B Background, SBAR technique, 816 Background information. See also Drug information queries; Ethics ethical analysis of dilemmas, 576-577 patient data and, 35, 37, 39, 39t, 40, 43t, 44, 46, 47, 48, 51, 54 Bad Ad program, 594, 1011, 1012, 1025, 1030-1031 BADCAT. See Butler University Adverse Drug Reaction Causality Assessment Tool Baker v. Arbor Drugs, Inc., 509 Bar code verification, 730, 1048, 1056, 1057, 1058 Barriers clinical practice guideline implementation, 337-338, 338t, 340 to effective responses and recommendations, 37 Basic & Clinical Pharmacology, 64t, 74 Basic Books v. Kinko’s Graphic Corp., 541 Basic Clinical Pharmacokinetics, 64t, 73-74 Basic controlled trials. See Controlled clinical trials; Study designs Basic Resources for Pharmacy Education listing, 62 Bayesian approach ACTs, 204-205 ADRs, 749, 765 Bell curve. See Normal distribution Belmont Report, 837 Benazepril, 126 Beneficence, 574, 575, 580, 585, 586, 586f Benefits and harms, of health care interventions, 312-314. See also Evidencebased clinical practice guidelines

1274

INDEX

Benefit-to-cost ratio, 280, 281t, 282 Benzocaine-induced methemoglobinemia, 9 Benzodiazepine, 645, 726 Berkson bias, 206f, 222 Berne Convention, 539, 545 Bernoulli trial, 366, 368t Best supportive care (BSC), 287, 297, 299t Best supportive care versus Oncoplatin and Oncotaxel (case study), 297-301, 298t, 299t Beta (β) rate, 140-141 Between-group differences testing nonparametric tests, Mann-Whitney test, 138, 251, 399, 414, 415, 416 parametric tests ANCOVA, 138, 381, 384, 395t, 398, 412-414, 435 factorial between-groups ANOVA, 395t, 410-412, 420, 421, 422 independent-samples t test, 135, 138, 381, 384, 391, 395t, 400, 407-408, 409, 414 Bextra. See Valdecoxib Beyond basic controlled trials. See Controlled clinical trials; Study designs Biases. See also Errors in ACTs, 205, 206t-207t, 208 admission, 206f, 222 Berkson, 206f, 222 blinding and, 125-127, 125t in case-control studies, 222-223 correlation, 207t data analysis, 207t data dredging, 207t diagnostic review, 222-223 exposure suspicion, 207f, 214t family history, 206f gender, 120 in health outcomes research, 250 information, 206t, 219 investigator, 113 language, 238 in meta-analyses, 239-241 Neyman, 206f nonresponse, 206f, 230 observer, 201 post hoc significance, 207t prevalence-incidence, 206f protopathic, 223 publication, 110, 164, 165, 237-238, 239, 240, 240f, 241, 245, 329-330 randomization and, 129 recall, 207f, 223 reference, 237 selection, 119-120, 206t, 217-218, 220, 238, 240, 325 significance, 207t in study title, 114 unmasking, 206f

Bibliography of clinical trials, 110t, 162-163 professional writing projects, 474-475 software, 474-475 Bilirubin, 514 Billings-Dr. Ford example, 582-587 Binomial distribution, 366-367, 368, 368t, 377, 406 Binomial test, 395t, 406 Bioavailability/Pharmacokinetics section, of drug evaluation monograph, 689 Biocreep. See Placebo creep Bioequivalence trials, 190t, 210-212 Biologics license application (BLA), 832, 843, 844, 845 BioMed Central, 11 Biomedical ethics. See Ethics Biomedical/pharmacy literature. See Controlled clinical trials; Drug information resources Biosimilar substitution, 635-636 BIOSIS Previews, 64t, 85 Birth deformities, 835 Bismuth subsalicylate, 162 BLA. See Biologics license application Blame, culture of, 777, 802, 804 Blinding, of clinical trials, 125-127, 125t Blogs, 936t, 1060 Blood pressure, dark chocolate and, 244-245 Blood urea nitrogen (BUN), 49, 52, 55, 385, 427 Body section. See Drug evaluation monographs; Professional writing Boolean operators, 83-84, 84f Boxplots, 357, 362, 363f Breast cancer Kaplan-Meier method and, 443-445, 443f, 444f Oncoplatin vs.Oncotaxel, 297-300, 299t oprelvekin (Neumega®), 47-50 Breast-feeding, levofloxacin, 75-76 Brigg’s. See Drugs in Pregnancy and Lactation British Pharmacopoeia, 73 Brody’s Human Pharmacology: Molecular to Clinical, 64t, 74 Bronchitis, Pelargonium sidoides and, 256-257 BSC. See Best supportive care BUN. See Blood urea nitrogen Butler University Adverse Drug Reaction Causality Assessment Tool (BADCAT), 748-749, 750

C CAHPS program. See Consumer Assessment of Healthcare Providers and Systems program Calcium absorption-vitamin D study, 376-377 Calcium channel blocker, 353, 683 CAM. See Complementary and alternative medicine

INDEX

Canadian Coordinating Office of Health Technology Assessment, 302t Cancer ALLCure drug, 844 anticancer agents, in various stages of drug development, 14 breast cancer Kaplan-Meir method and, 443-445, 443f, 444f Oncoplatin vs.Oncotaxel, 297-300, 299t oprelvekin (Neumega®), 47-50 cervical, 215 chemotherapy BSC versus Oncoplatin and Oncotaxel (case study), 297-301, 298t, 299t chemotherapy-induced peripheral neuropathy, 169-170 double checks, 818 error, 803 regime library, medical informatics, 20 thrombocytopenia with, 48, 50 VP-CAP comparison, 287 clinical trials Internet recruitment, 121 placebos, 123 endometrial, 220 European Organization for Research and Treatment of Cancer, 247 Functional Assessment of Cancer Therapy, 247 Functional Living Index-Cancer, 247 lung cancer studies, 215f, 221f, 222, 223, 372, 403 medical informatics and, 20 National Cancer Institute, 86, 857 National Comprehensive Cancer Network’s Drugs and Biologics Compendium, 529, 855 non-small cell lung cancer, 287 off-label drug use and, 529-530 placebos and, 123 SCORxE, 1033 screenings, CDSS, 1054 testicular, 468 uterine, 223 venous thrombosis, 129 Cancer Epidemiology, Biomarkers, and Prevention, 220 Cancer Today, 89 CANCERLIT, 86 CAP. See Cyclophosphamide, doxorubicin, and cisplatin CAPE. See Center for the Advancement of Pharmaceutical Education Captopril, 129 Carbamazepine, 201, 202, 766 Care. See also Health care; Managed care pharmacy; Quality improvement continuum of, 8, 814, 885 due care, 506, 509, 518, 537 reasonable care, 507-508, 513

1275

Career and leadership opportunities. See Drug information specialists CareNotes System, 913 Casarett & Doull’s Toxicology: The Basic Science of Poisons, 64t, 77 Case history studies. See Case-control studies Case reports, 190t, 226-227 Case series, 190t, 226-227 Case studies ADRs, 749-750, 764 BSC versus Oncoplatin and Oncotaxel, 297-301, 298t, 299t cohort study design, 220 controlled clinical trials, 159-160, 168-170 drug evaluation monographs, 693 drug information education and training, 964 drug information in ambulatory care, 910-911, 919, 922-923 drug information resources, 68, 75-76, 78, 81, 88-89, 92-93, 95 drug promotion, 1024, 1030, 1031, 1034 ethical issues, 582-592 evidence-based clinical practice guidelines, 320, 325, 334, 340 investigational drugs, 844, 848, 851-852 legal aspects of DI practice, 507, 508-509, 540, 548 medication errors, 797, 802, 806, 819-820 meta-analyses, 244-245 natural products, 256-257 pharmaceutical industry, 980-981, 984, 985, 992, 993, 999-1000 pharmacy informatics, 1060, 1061, 1064 professional writing, 477, 485, 493 P&T committees, 624-625, 639, 649-650 quality improvement, 717, 724, 726 response-recommendations for drug information queries, 46-57 as study design type described, 226-227 N-of-1 trials compared to, 202t, 226 purpose, 190t TXA-EACA comparison, 399-400 vitamin D-calcium absorption, 376-377 Case Studies in Pharmacy Ethics, 593 Case-control studies (case-referent studies, case history studies, retrospective studies), 190t, 221-224, 221t, 224t Cases. See Court cases Catalog of Teratogenic Agents, 64t, 75 Categorical distribution, 368t Categorical variables, 356 Causality, of ADRs, 745-751 Cause-and-effect relationships ADRs, 745, 751 controlled clinical trials, 117, 121, 188, 190t, 213 observational study designs, 188, 213 survey research, 188, 227 CBA. See Cost-benefit analysis

1276

INDEX

CBER. See Center for Biologics Evaluation and Research CBS, 92t CCA. See Cost-consequence analysis CDC. See Centers for Disease Control and Prevention CDER. See Center for Drug Evaluation and Research CDRH. See Center for Devices and Radiological Health CDS. See Clinical decision support CDSSs. See Computer-based clinical decision support systems CDTM. See Collaborative drug therapy management CEA. See Cost-effectiveness analysis Cecil Medicine, 64t, 76 Celecoxib (Celebrex), 115-116, 143, 154, 807. See also CONDOR trial Cell phones. See Mobile health Center, drug information and, 4 Center for Biologics Evaluation and Research (CBER), 831 Center for Devices and Radiological Health (CDRH), 831 Center for Drug Evaluation and Research (CDER), 831, 998 Center for Food Safety and Applied Nutrition, 753 Center for the Advancement of Pharmaceutical Education (CAPE), 8, 18 Center for Veterinary Medicine (CVM), 79 Centers for Disease Control and Prevention (CDC) adverse drug events, 783t COSTEP program and, 1001 drug information Web site, 915 emergency room visits statistic, 903 immunization information source, 920 mobile apps, 908 Morbidity and Mortality Weekly Report, 228 National Center for Immunization and Respiratory Diseases, 920 patient and medication safety, best practices, 819t purpose, 987t suggested reading, 928 tutorials, 916 VAERS program, 764 Centers for Medicare and Medicaid Services (CMS), 906-907, 921, 1046. See also Meaningful Use program Central limit theorem, 352, 385-386, 393, 397, 405, 406, 408, 409, 411, 413, 417, 420, 422 Central tendency, 135-136, 352, 357 CER. See Cost-effectiveness ratio Certified specialist in poison information, 22 Cervical cancer, 215 CFR. See Code of Federal Regulations

CGMPs. See Current Good Manufacturing Practices Chance nodes, in decision trees, 293, 294 Chapter 797 Pharmaceutical Compounding— Sterile Preparations, 878-879 CHD. See Coronary heart disease Check step, PDCA Cycle, 706 Chemical Type classification, FDA, 617, 617t Chemotherapy BSC versus Oncoplatin and Oncotaxel (case study), 297-301, 298t, 299t chemotherapy-induced peripheral neuropathy, 169-170 double checks, 818 error, 803 regime library, medical informatics, 20 thrombocytopenia with, 48, 50 VP-CAP comparison, 287 CHI. See Consumer health information Childhood Autism Spectrum Test, 415 Children’s Hospital of Philadelphia, Extemporaneous Formulations, 68 Chi-square test. See Mantel-Haenszel chi square test; Pearson’s chi square test Chocolate, blood pressure and, 244-245 Choice nodes, in decision trees, 293, 294 Cholesterol, mean serum total cholesterol, 404-405. See also Low-density lipoprotein-cholesterol Chondroitin, 95 Chronic obstructive pulmonary disease (COPD), 122, 914 CINAHL, 86, 324, 876 CIs. See Confidence intervals Cisplatin, 287 Citations, in professional writing projects, 468, 471, 472 Civil liability, 520 Claritin, 527 Classroom Guidelines, 540, 541 Clear Health Communication Initiative, Pfizer, 946, 954 Clemastine fumarate/phenylpropanolamine hydrochloride (Tavist-D), 509 Clin-Alert, 751 Clinical call, 284 Clinical decision support (CDS), 1053-1054. See also Computer-based clinical decision support systems Clinical decisions/recommendations, study designs, 188, 257-259, 258f. See also Study designs Clinical difference assessment of, 152-154 statistical significance versus, 150-152, 151f Clinical investigation, 832 Clinical managers, 505. See also Drug information specialists Clinical outcomes, CBA, 280 Clinical pathways, recommendation for, 680-681

INDEX

Clinical Pharmacology clinicalpharmacology.com, 63t, 64t, 65, 72, 94, 529, 530, 911, 912-913 Small Animal Clinical Pharmacology and Therapeutics, 80 Clinical Pharmacology OnHand, 82 Clinical pharmacy concept, 7 Clinical practice guidelines. See also Evidencebased clinical practice guidelines consensus-based, 245-246 EBM and, 245-246, 314-316 introduction, 312-314 purpose, 246 Clinical protocol, for IND, 839 Clinical relevance, of clinical trial results, 161-162 Clinical significance alpha and p values, 387-388 confidence intervals, 389-391, 389f, 390f effect size and, 391-392 statistical significance versus, 352, 387-392 varying definitions, 392 Clinical study designs. See Study designs Clinical support. See Computer-based clinical decision support systems Clinical trials. See Controlled clinical trials; Investigational drugs; Phases of clinical trials; Study designs ClinicalTrials.gov registry, 11, 63t, 165, 237, 836 Clonidine, 51, 52, 83, 84, 84f Clopidogrel, 120, 1021 Closed cohort, 216-217 Closed formularies, 633-635 Clostridium difficile, 753, 794t Cluster sampling, 354 Clustering, 339, 398, 400, 458 CMA. See Cost-minimization analysis CME. See Continuing medical education CMS. See Centers for Medicare and Medicaid Services CNN, 91, 92t CNS Disorders Today, 89 Coauthors, 462-463 Coca-Cola Co v. Purdy, 536-537 Cocaine solution, 517 Cochran Q test, 395t, 426-427 Cochrane Collaboration, 302t, 342 Cochrane Database of Systematic Reviews, 688t, 910 Cochrane Library, 86, 244, 324, 341 Cochran-Mantel-Haenszel chi square test. See Mantel-Haenszel chi square test Cocoa products, blood pressure and, 249-250 Code of Federal Regulations (CFR) ethical conduct of clinical trials, 837 gpoaccess.gov/cfr/index.html, 75, 837, 869 monitoring of trials, 131 pharmacy law, 64t, 75 prescription drug advertising regulations, 1017 Title 21, 837, 978, 984, 1017

1277

Code on Interactions With Health Care Professionals, 552t, 559, 972, 981, 1063 Coffee consumption, endometrial cancer, 220 COGS. See Conference on Guideline Standardization Cohen’s kappa. See Kappa statistic Cohort studies (longitudinal studies), 190t, 214-220, 214t, 215f, 397 Coin flipping, 367, 406, 858 Colchicine, 510 Collaborative drug therapy management (CDTM), 511-512 Colleges of Pharmacy, pharmacoeconomics educational opportunities, 302 Commentaries, on clinical trials, 165-168 Commenting, professional writing, 469 Commercial IND, 832 Commissioned Corps Officer Student Training and Extern Program (COSTEP), 1000-1001 Committee on Standards for Developing Trustworthy Clinical Practice Guidelines, 316t Committee on Standards for Systematic Reviews of Comparative Effectiveness Research, 316, 317t Common Formats, 787-788, 790, 793, 821 Communication ADR monitoring process, 755 medication information specialist and, 8 privacy in, 549-550 within P&T committees, 650-652 Community pharmacy practice, and drug information, 929-954 case studies, 937-938 conclusion, 947-948 health literacy, 945-946, 946t introduction, 930-932 patient sources of drug information mobile health, 938, 939t-940t, 940-942, 941t patient education versus CHI, 933-934 social media, 10-11, 934, 935t-936t, 937-939 trends, 931-932 Web 2.0, 929, 934 wisdom of crowds, 930, 937 pharmacists as drug information providers discuss with patient about pharmacists as DI source, 930, 943-944 information therapy, 946-947 new model of drug information, 942-947 passive process, 932-933 progressive drug information services, payment, 947 pharmacy informatics, 1057-1058 self-assessment questions, 948-951 suggested readings, 954 Web sites evaluation, 944-945, 945t health literate, 946t Community rule, 506

1278

INDEX

Comparative Effectiveness Plus tool, IDIS, 87. See also Iowa Drug Information Service Comparative negligence, 518 Compendia, major, 63t, 64t, 82 Compendium of Pharmaceuticals and Specialties, 73 Compendium of Veterinary Products (CVP), 64t, 79 Complementary and alternative medicine (CAM) Clinical Pharmacology database, 912 in drug evaluation monograph, 673 growing use of, 16-17 National Center for Complementary and Alternative Medicine, 94t Natural Medicines Comprehensive Database, 914 resources on, 17, 94t Compliance. See Adherence Composite endpoints, 129-130, 148, 166 Compounding, veterinary, 79-80 Computer Software Act of 1980, 543 Computer technology. See Informatics; Information technology Computer-based clinical decision support systems (CDSSs). See also Pharmacy informatics clinical practice guidelines implementation through, 339 medical records, 12 pharmacy informatics, 1047, 1048, 1050, 1053-1054, 1055 Computerized provider order entry (CPOE), 3, 20, 646, 786, 814, 816, 1048, 1050-1051 Conclusion section clinical trials, 110t, 149-150 professional writing projects, 473-474 Concurrent cohort study. See Prospective cohort study Concurrent data collection, for MUE, 729 Concurrent negligence, 518 CONDOR trial, 115-116, 118, 126, 130-131, 133, 141, 143, 166. See also Controlled clinical trials Conference on Guideline Standardization (COGS), 470 Confidence intervals (CIs) controlled clinical trials, 154-156 NI trials, 193 statistical significance and, 389-391, 389f, 390f Confidentiality principle, 581, 586f Conflict of interest ACPE accreditation standards, 957 in clinical practice guideline development, 316, 317t, 319 commercial support of educational activities, 552 in document submission, 475 in formulary management, 632 IRBs and, 851 policies, drug promotions, 1028

in published research, 113, 164 referees, 476 Wikipedia entries, 531-532 Confounders, 122, 128, 207t, 217, 218-219, 355 Consensus-based clinical practice guidelines, 245-246. See also Evidence-based clinical practice guidelines Consent ethics inquiry and, 581, 586f informed consent of clinical trial subjects, 124-125 off-label use and, 527-530, 528t Consequential damages, 516 Consequentialist theory, 574-575, 579, 580, 581, 585, 586, 589 Consolidated Standards of Reporting Trials (CONSORT), 109, 199, 469 Constancy assumptions, 196-197 Constraints, for writing newsletters and Web sites, 478-479 Consumer Assessment of Healthcare Providers and Systems program (CAHPS), 712 Consumer health information (CHI). See also Community pharmacy practice on Internet, 93-94, 94t local libraries, 93 patient education versus, 933-934 social media for, 10-11, 934, 935t-936t, 937-939 Consumer “privacy bill of rights,” Obama administration, 550 Consumer Reports, 935t Content, of newsletters and Web sites, 483 Contingency tables, 372, 372f, 401, 402, 403, 415 Continuing medical education (CME) ACCME, 504, 551-552 described, 336, 504, 551, 977 Continuous attribute, 706t Continuous quality improvement (CQI), 312, 340, 706t, 709, 719-720. See also Quality improvement Continuous variables, 356 Continuum, of care, 8, 814, 885 Contraceptives, 127, 512, 525, 596, 597, 692, 992 Contract drug information centers (fee-for-service), 19-20 Contract research organization (CRO), 832 CONTRAST study. See Evaluation of Corlopam in Patients at Risk for Renal Failure-A Safety and Efficacy Trial Control groups controlled clinical trials, 121-124 historical controls, 122, 123, 832 investigational drugs, 832 Control limits, for MUE, 704, 714, 727-728, 727f, 730 Controlled clinical trials, 105-186. See also Randomized controlled trials; Study designs

INDEX

adherence in, 131-132, 143-145 biomedical/pharmacy literature and, 108-110, 108t, 110t case studies, 159-160, 168-170 cause-and-effect relationships, 117, 121, 188, 190t, 213 ClinicalTrials.gov registry, 11, 63t, 165, 237, 836 commentaries/critiques, 165-168 conclusion, 168 conclusion section, 110t, 149-150 CONSORT format, 109, 199, 469 content and format of, 110t control group, 121-124 crossover designs, 378, 382-383 data presentation, 134-139, 135t, 136f, 137f defined, 109, 117 discussion section, 110t, 149-150 evaluation of published studies abstract, 110t, 114-115 acknowledgments, 110t, 163 approach to, 111-113 bibliography, 110t, 162-163 discussion/conclusion section, 110t, 149-150 introduction section, 110t, 115-116 journals, peer-review, and investigators, 111-113 methods section, 110t, 116-142 results interpretation, 110t, 150-162 results section, 110t, 142-148 title, 114 experimental designs as, 374-375 format and content of, 110t fundamental elements, 111 funding of, 110t, 163-165 goal, 109 intervention group, 121-124 interventional trials, 188, 213 introduction, 107-108 ITT approach, 144-145, 168, 197, 256, 380 methods section, 110t, 116-142 blinding, 125-127, 125t control groups, 121-124 endpoints, 129-131, 145-146 follow-up schedule/data collection/ adherence, 131-132 intervention group, 121-124 patient inclusion/exclusion criteria, 118-121 power analysis, 132-134, 139-142 randomization, 127-129 sample size, 132-134 statistical analysis, 134-139, 135f, 136f, 137f subject consent, 124-125 Type I and Type II errors, 139-142, 140t, 386-387 negative studies, 109-110, 164 origins of, 109 overview, 108-110 parallel-groups design, 117, 377-378, 380-382

1279

per-protocol approach, 144-145, 380 poorly designed, 109, 237, 252 protocol based, 377 purpose, 190t quasi-experimental designs compared to, 374, 375-376 results section, 110t, 142-148 ancillary compared with adjunctive therapies, 148 clinical difference assessment, 152-154 clinical relevance assessment, 161-162 confidence intervals, 154-156 endpoints/safety, 145-146 interpretation, 110t, 150-162 interpreting risks and numbers-needed-totreat, 156-159, 157t no difference compared with equivalency, 160-161 statistical significance versus clinical difference, 150-152, 151f subgroup analysis, 147-148 subject demographics, 142-143 subject dropouts/adherence, 143-145 selective reporting, 109, 251 self-assessment questions, 170-174 study design, 117-118 suggested readings, 185-186 unpublished studies, 209-210, 237-238, 532, 538, 539 Convenience sampling, 354 COPD. See Chronic obstructive pulmonary disease Copyright Berne Convention, 539, 545 Copyright Act of 1976, 538, 539, 541, 543, 544 Copyright Clearance Center, 541, 542, 543 Copyright Term Extension Act, 538 copyrighted material and permission, 466t, 468, 471, 472, 473, 489 fair use, 536, 539-542, 544, 545 fictitious reporting, 545 intellectual property rights, 537-546 ownership, 538-539 plagiarism and, 471-473, 545-546, 851 protection, 537-538, 538t TEACH Act, 472, 544, 544t, 568 Coricidin, 78 Coronary heart disease (CHD), 159, 322 Corrective actions, for MUE, 731-732 Correlation bias, 207t Correlational analysis Pearson’s product-moment correlation, 396t, 428-430, 429f, 431, 432, 434, 436, 437, 441, 450 Spearman rank-order correlation coefficient, 396t, 428, 436-437 Cost in pharmacoeconomic equation, 275f RCTs, 375

1280

INDEX

Cost assessment. See Pharmacoeconomics Cost comparison, drug evaluation monograph, 691-692 Cost control, for added-with-restrictions, 679-680 Cost-benefit analysis (CBA), 274, 275, 276t, 278-282, 279t, 281t, 285, 287, 289, 296 Cost-consequence analysis (CCA), 283 Cost-effectiveness analysis (CEA), 274, 275, 276t, 282-284, 282t, 283t, 284t, 287, 289, 293, 296 Cost-effectiveness grid, 282-283, 282t, 284 Cost-effectiveness ratio (CER), 282-284, 283t, 284t, 287, 294, 295t, 300 COSTEP. See Commissioned Corps Officer Student Training and Extern Program Cost-minimization analysis (CMA), 274, 275, 276t, 278-279, 279t, 287, 289, 296 Cost-utility analysis (CUA), 273, 274, 276t, 285-287, 286t, 289, 292, 296, 300-301 Coumadin, 92-93 Council of Science Editors, 465 Counterfeit drug products, P&T committees and, 647 Court cases. See also Legal aspects, of drug information practice Association of American Publishers v. New York University, 541 Baker v. Arbor Drugs, Inc., 509 Basic Books v. Kinko’s Graphic Corp., 541 Coca-Cola Co v. Purdy, 536-537 Daniel v. Dow Jones & Co., Inc., 516 Delmuth Development Corp. v. Merck & Co., 515 Dooley v. Everett, 508 Fidelity Leasing Corp. v. Dun & Bradstreet, Inc., 521 Frye v. Medicare-Glaser Corporation, 509 Greenmoss Builders v. Dun & Bradstreet, 516 Hand v. Krakowski, 508 Happel v. Wal-Mart Stores, 510 Harbeson v. Parke Davis, 518-519 Harper & Row Publishers, Inc. v. Nation Enterprises, 539 Heredia v. Johnson, 511 Horner v. Spalitto, 510 Jeppesen case, 516 Jones v. J.B. Lippincott Co., 515 Libertelli v. Hoffman La Roche, Inc. & Medical Economics Co., 515 Marchione v. State, 511 Morgan v. Wal-Mart Stores, 510 New York Times v. Tasini, 543 Parkas v. Saary, 509 Perez v. Wyeth Laboratories Inc., 525, 526 Playboy Enterprises, Inc. v. Universal Tel-A-Talk, Inc., 536 Princeton University Press v. Michigan Document Services, Inc., 541

In re Michael A. Gabert, 513 In re Pharmatrak Inc. v. Privacy Litigation, 550 Reben v. Ely, 517 Reeves v. Pharmajet, Inc., 512 Roman v. City of New York, 515 Sanderson v. Eckerd Corporation, 510 Silverstein v. Penguin Putnam, 542 Thompson v. Western States Medical Center, 526 Warner Bros. Ent’mt, Inc. v. RDR Books, 542 Williams & Wilkins Co. v. United States, 541 Winter v. G.R. Putnam’s Sons, 515 Covariates, 352, 355, 394. See also Analysis of covariance Covenant, ethical, 581 Coverage errors, 229 Cox and Snell R2, 440 Cox regression (Cox proportional-hazards model), 220, 441, 445-450, 448f CPOE. See Computerized provider order entry CQI. See Continuous quality improvement Credentialing and privileges, P&T committees, 644 Credit, in professional writing projects, 463, 464, 468, 471, 472, 481 Criteria, MUE and, 717-718, 723-725, 725t Critical factor assessment, for making responses and recommendations, 40-41, 42t-43t, 43 Critical Path Initiative, 203, 836 Critical pathways, recommendation for, 680-681 Critiques, of clinical trials, 165-168 CRO. See Contract research organization Cross-Cultural Perspectives in Medical Ethics, 575 Crossing the Quality Chasm, 810 Crossover designs, 378, 382-383. See also Controlled clinical trials; Randomized controlled trials Cross-sectional studies, 190t, 214t, 224-226, 397 Crushing drugs, case study, 540 CUA. See Cost-utility analysis Culture of blame, 777, 802, 804 Culture of safety, 803, 814, 819 Culture of shame, 777, 802, 804 Cultures in ethical analysis, 577 health outcomes research and, 251-252 Just Culture, 802-806 Current Contents Connect, 63t, 86, 324 Current Good Manufacturing Practices (CGMPs), 209, 835 Customer focused, CQI, 706t Customer response center, 974-975 CVM. See Center for Veterinary Medicine

INDEX

CVP. See Compendium of Veterinary Products Cybermedicine, liability concerns for, 533-535 Cyclophosphamide, 48, 287 Cyclophosphamide, doxorubicin, and cisplatin (CAP), 287

D Dabigatran (Pradaxa), 556, 683-684 Daniel v. Dow Jones & Co., Inc., 516 Darvon. See Propoxyphene Data. See also Statistical analysis; specific data data presentation, controlled clinical trials, 134-139, 135t, 136f, 137f factual, survey research, 231 Data analysis, for MUE, 730-731 Data analysis bias, 207t Data collection in controlled clinical trials, 131-132 errors, in cross-sectional studies, 225 for MUE, 728-730 survey research, 228-229 Data dredging bias, 207t Data driven, CQI, 706t Databases clinicalpharmacology.com, 63t, 64t, 65, 72, 94, 529, 530, 911, 912-913 for responses-recommendations to drug information queries, 40-41, 42t-43t, 43 secondary systems BIOSIS Previews, 64t, 85 CANCERLIT, 86 CINAHL, 86, 324, 876 Cochrane Library, 86, 244, 324, 341 Current Contents Connect, 63t, 86, 324 EMBASE, 63t, 64t, 86, 108t, 237, 254, 324, 508, 910 Google Scholar, 87, 90, 531, 532, 533 IDIS, 63t, 64t, 83, 87, 237, 877, 909 IPA, 63t, 64t, 87, 108t, 114, 237, 876 Journal Watch, 87, 167 LexisNexis, 63t, 64t, 88, 91, 108t MEDLINE, 5, 63t, 64t, 86, 88, 108t, 237, 254, 324, 341, 349, 508, 532, 543, 910 overview, 83-85 Pharmacoeconomics & Outcomes News Weekly, 88 PubMed, 11, 88, 108t, 114, 166, 237, 254, 508, 532, 876, 909, 910 Reactions Weekly, 63t, 64t, 88, 751 synchronization, P&T committees and, 652 DDI. See Division of Drug Information Dechallenge, in ADRs, 41t, 746, 747-748, 749 Decision analysis. See Pharmacoeconomic analysis

1281

Decision making, evidence-based, 588, 630 Decision Tree for determining the Culpability of Unsafe Acts, 805 Decision trees. See also Pharmacoeconomic analysis; Statistical tests pharmacoeconomic analysis, 288t, 290-291, 290t, 291f, 292, 293, 294, 295 statistical tests selection, 394-400, 395t, 396t DecisionPro, 297 Decisions and ethics. See Ethical decision making Declaration of Helsinki, 837 Defenses, to negligence. See Negligence Definitions section, in policies, 879-880 Degrees of freedom (df), 393, 401, 402, 405, 408, 409, 421 Delmuth Development Corp. v. Merck & Co., 515 δ. See Absolute difference in the effect Deming Cycle, 706. See also Plan-Do-Check-Act Demographics, of clinical trial subjects, 142-143 Deontological theories, 574, 575, 579, 580, 581, 585, 586, 589 Department of Defense Pharmacoeconomic Center, 302t Department of Health and Human Services. See Health and Human Services Dependent variable (DV), 355 Descriptive statistics, 357-363 central tendency measures, 135-136, 352, 357 graphical representations, 359f, 361f, 362-363, 363f inferential statistics compared to, 357 shape measures, 360, 361f, 362 variability measures, 358, 359f, 360 Descriptive surveys, 228 Design, of newsletters and Web sites, 480-482 Desipramine, 510 Dexlansoprazole, 639 Dextrose, 754t, 794t df. See Degrees of freedom DHHS. See Health and Human Services DI. See Drug information DI PRN. See Drug Information Practice and Research Network DI specialists. See Drug information specialists Diagnostic review bias, 222-223 Dialysis treatment, quasi-experimental designs and, 375, 376 Diazepam (Valium), 515, 918t Diccionario de Especialidases Farmaceuticas, 73 Dichotomous variables, 356 Diclofenac, 115. See also CONDOR trial DICs. See Drug information centers Dictionary Vidal, 73 Didanosine-dapsone, 514 Dietary Supplement and Nonprescription Drug Consumer Protection Act of 2006, 762

1282

INDEX

Dietary supplements (DSs) ADR reporting, 761-763 medical literature, 252-257 duration of studies, 254 evidence lack, 254-255 international trials and information retrieval, 253-254 quality and purity, 255 size of trials, 254 special considerations, 255-256 standardization of supplements, 253 testing natural products (case study), 256-257 Natural Medicine Comprehensive Database, 63t, 69-70 Natural Standard, 63t, 70 PDR for Herbal Medicines, 63t, 66, 70 resources on, 52t, 69-70 Review of Natural Products, 63t, 70 Dipeptidyl peptidase-4 inhibitor, 200 Diphenhydramine, 754t, 794t Direct and simple, professional writing, 466t, 467-468 Direct medical costs, 273, 276, 277, 279, 283, 289, 300 Direct nonmedical costs, 273, 276, 277, 279 Directory of Residencies and Fellowships, ACCP, 963 Direct-to-consumer advertising (DTCA), 1014-1025. See also Drug promotions defined, 1014 evaluating, 1023-1025, 1023t-1024t, 1024t-1025t help-seeking ads, 1012, 1015-1018, 1024, 1025t improvement suggestions, 1022-1023 information not required for inclusion, 1016t legal aspects of, 524-526, 1017-1018 opposition, 1020-1022 PhRMA guidelines for, 1018, 1019t product claim ads, 1012, 1015-1018, 1024t reminder ads, 1012, 1015-1018, 1025t support, 1018-1020 types, 1015-1016 Disciplinary actions, with MUE, 731 Discount rate, 277-278, 282, 291, 295 Discrete variables, 356 Discussion questions, 652-653, 802 Discussion section, controlled clinical trials, 110t, 149-150 Disease factors, in making responses and recommendations, 42t Disease states, veterinary, 81 Disease-specific instruments, HR-QOL measurements, 247, 248 Dispense As Written, 637 Dispensing in medication use process, 719t pharmacy informatics and, 1055 Dispersion. See Variability Disposal, of unused medication, 916-919, 918t Distribution shape, measures, 360, 361f, 362

Distribution system, newsletters, 483-484. See also Professional writing Distributions. See Probability distributions Division of Drug Information (DDI), 973, 993-1000. See also Food and Drug Administration; Pharmaceutical industry CDER and, 998 drug information resources, 995, 996t-997t, 998 drug information services, 994t-995t social media and, 998-999 visitors, 994t DMF. See Drug master file Do step, PDCA Cycle, 706 Doctrine of drug overpromotion, 526-527 Documentation responses and recommendations, 44-45 section, of policies, 881 Documents, professional writing. See Professional writing Donabedian framework, 704, 708-709, 736. See also Quality improvement Dooley v. Everett, 508 Dosage forms, availability of, 63t, 68 recommendations geriatric, 63t, 71 organ impairment, 63t pediatric, 64t, 73 veterinary, 78-79 Dosage Form section, of drug evaluation monograph, 689-690 Double-blinding, 125t, 126 Double-dummy methods, 126, 198 Doxorubicin, 48, 287, 813 DPL (decision analysis software), 295 Dropouts, in controlled clinical trials, 143-145 Drug. See also Investigational drugs Rx, in pharmacoeconomic equation, 275f shortages, 648-650 Drug approval process. See Investigational drugs Drug class reviews, 20, 669, 671, 672, 681 Drug errors. See Medication errors Drug evaluation monographs, 669-701. See also Pharmacy and Therapeutics committees ASHP guidelines, 671-672, 678-679 body of, 682-692 Bioavailability/Pharmacokinetics section, 689 cost comparison section, 691-692 Dosage Form section, 689-690 Known Adverse Effects/Toxicities section, 690 Patient Information section, 690-691, 691 Patient Monitoring Guidelines, 691 Pharmacologic Data section, 683 Therapeutic Indications section, 683-689 case studies, 693 complementary/alternative medicine in, 673

INDEX

effort and time in preparation, 670, 672, 693 introduction, 670-673 purpose of, 670-673 recommendations, 676-682 ASHP guidelines, 678-679 essence of, 682 objections to, 677 objective evidence for, 677-678 removal of agents, 681-682 restrictions, 678-680 therapeutic interchange, 682 self-assessment questions, 694-696 summary page, 673-682 AHFS classification, 673-674 FDA classifications, 673, 674-675, 674t, 675t format of, 673 Therapeutic Indications section (in body section) clinical guidelines subsection, 684 clinical study abstracts subsection, 684-685 indications subsection, 683-684 pharmacogenomics, 685-686 quality of life studies, 689 summary of evidence table, 686-687, 686t, 687t, 688t time and effort in preparation, 670, 672, 693 value of, 693 Drug Facts and Comparisons, 65, 70, 71, 912 Drug formularies. See Formularies Drug formulary system. See Formulary system Drug informatics, 3, 20. See also Pharmacy informatics Drug information (DI). See also Community pharmacy practice; Drug information in ambulatory care; Drug information specialists; Ethics; Legal aspects; Medication information contexts of term, 3 development of term, 4 education and training, in APPEs, 18, 24, 955, 958-959, 964 introduction to concept, 1-34 meanings, 3 new model of, 942-947 quality improvement, 732-734 self-assessment questions, 25-28 services, 4t suggested readings, 34 Web sites for, 914-915 Drug information centers (DICs) contract drug information centers (fee-for-service), 19-20 evolution, 4-9 Drug information education and training, 955-970 ACPE standards, 24, 551, 955, 957, 958, 959 APPEs, 18, 24, 955, 958-959, 964 case study, 964 conclusion, 964-965

1283

DI concepts for professional degree curricula, 959t evaluation and application skills, 956 foundation skill development, 956-960 information retrieval, 7, 955, 956, 960 introduction, 956 IPPEs, 24, 958 PGY1 programs, 956, 960, 962, 963 PGY2 programs, 956, 961-962, 970 pharmacist’s role, 17-19 self-assessment questions, 965-968 specialized skill development, 960-963 specialty training, 963-964 suggested readings, 970 Drug Information Handbook, 66, 912 Drug information in ambulatory care, 899-928 access considerations for, 915 ambulatory care clinician responsibilities, 904-916 case studies, 910-911, 919, 922-923 evidence-based clinical practice guidelines, 908-911 formulary information, 905-908 immunization information, 919-920 introduction, 900-901 patient searches, 902-904 QA considerations, 921-923 reasons for focusing on, 901 resources desired characteristics, 911 review, 911-915 self-assessment questions, 923-926 suggested readings, 928 unused medication disposal, 916-919, 918t Drug Information Practice and Research Network (DI PRN), 18, 958, 961 Drug information provision (medication information provision). See also Community pharmacy practice drug policy development, 13-17 evolution, 1, 5-19 contract drug information centers (fee-for-service), 19-20 DICs, 5-9 pharmacist’s role adverse drug events, 9-10 education of pharmacy students, 17-19 importance, 2-3, 15, 25 information technology, 12-13 medication information skills, 8, 8t, 25 medication literature evaluation skills, 13-14 medication therapy, sophistication of, 14-15 new health information technologies, 10-13 outcomes research, 14 patient care areas, 7-8 self-care movement, 15-17

1284

INDEX

Drug information queries, responses and recommendations by pharmacists, 35-58 accepting responsibility and eliminating barriers, 37 analysis and synthesis of information, 44 assessing critical factors, 40-41, 42t-43t, 43 background information and patient data, 35, 37, 39, 39t, 40, 43t, 44, 46, 47, 48, 51, 54 building database, 40-41, 42t-43t, 43 case studies, 46-57 conclusion, 45 desired characteristics of, 44-45, 45t follow-up, 45 identifying genuine need, 37-40 introduction, 36 questions to consider in, 39-40, 39t, 41t self-assessment questions, 57 Drug Information reference book, AHFS, 65, 673-674 Drug information resources, 59-103. See also Consumer health information; National Library of Medicine; Primary literature advantages, 107 alternative resources, 90-93, 92t ambulatory care, 911-915 case studies, 68, 75-76, 78, 81, 88-89, 92-93, 95 conclusion, 95 controlled clinical trials and, 108-110, 108t, 110t disadvantages, 107 Internet, unreliable information, 112-113 introduction, 60-61 policy development and, 875-876 secondary BIOSIS Previews, 64t, 85 CANCERLIT, 86 CINAHL, 86, 324, 876 Cochrane Library, 86, 244, 324, 341 Current Contents Connect, 63t, 86, 324 EMBASE, 63t, 64t, 86, 108t, 237, 254, 324, 508, 910 Google Scholar, 87, 90, 531, 532, 533 International Pharmaceutical Abstracts, 63t, 64t, 87, 108t, 114, 237, 876 Iowa Drug Information Service, 63t, 64t, 83, 87, 237, 877, 909 Journal Watch, 87, 167 LexisNexis, 63t, 64t, 88, 91, 108t MEDLINE, 5, 63t, 64t, 86, 88, 108t, 237, 254, 324, 341, 349, 508, 532, 543, 910 overview, 83-85 Pharmacoeconomics & Outcomes News Weekly, 88 Reactions Weekly, 63t, 64t, 88, 751 self-assessment questions, 95-98 stepwise approach, 60 tertiary adverse effects, 63t, 67 availability of dosage forms, 63t, 68

by categories of drug information, 62, 63t-64t compounding/formulations, 63t, 68-69 dietary supplements, 63t, 69-70 dosage recommendations, general and organ impairment, 63t dosage recommendations, geriatric, 63t, 71 dosage recommendations, pediatric, 64t, 73 drawbacks of, 61-62 drug interactions, 63t, 70-71 drug of choice/therapy evaluation, 64t, 76-77 evaluation of, 62, 62t examples of, 61 format selection, 81-82 general product information, 63t, 65-67 geriatric dosage recommendations, 63t, 71 identification of product, 63t, 71-72 incompatibility and stability, 63t, 72 international drug equivalency, 64t, 72-73 lactation/teratogenicity, 64t, 75 for mobile devices, 82 for PDAs, 82 pediatric dosage recommendations, 64t, 73 pharmacokinetics, 64t, 73-74 pharmacology, 64t, 74 pharmacy law, 64t, 75 stability and incompatibility, 63t, 72 teratogenicity/lactation, 64t, 75 therapy evaluation/drug of choice, 64t, 76-77 toxicology, 64t, 77 veterinary medicine, 64t, 78-81 types, 108t WebMD® described, 936t FDA-WebMD joint online venture, 93 Medscape, 11, 64t, 67, 76, 167, 915 mobile app, 940t as tertiary resource, 108t Drug information specialists (medication information specialists). See also Pharmacists areas of practice, 25 clinical managers, 505 defenses to negligence employer defenses, 520 individual defenses, 518-519 malpractice protection, 512, 520-523, 522t defined, 4 information technology advances and, 2, 12-13 leadership and career opportunities academia, 19, 23-24 contract drug information center (fee-for-service), 19-20 HMOs, 19 managed care pharmacy, 20-21 medical informatics within health system, 20

INDEX

overview, 2, 19 PBMs, 19 pharmaceutical industry, 22-23 poison control, 21-22 scientific writing and medical communication, 19, 24 liability of for inappropriate analysis or dissemination of information, 517-518 for inappropriate quality information, 513-517 for incomplete information, 509-511 for negligence and failure to warn, 504, 508, 509, 523, 525, 527 training requirements, 5 Drug Interaction Facts, 63t, 71 Drug interactions ADR classification, 752 Baker v. Arbor Drugs, Inc, 509 Stockley’s Drug Interactions, 63t tertiary resources on, 63t, 70-71 Drug literature evaluation. See Controlled clinical trials; Study designs Drug master file (DMF), 832 Drug of choice/therapy evaluation, 64t, 76-77 Drug overpromotion, 526-527 Drug policy development, medication information provision and, 13-17 Drug Prescribing in Renal Failure, 63t Drug product, 832 Drug promotions, 1011-1044 academic detailing, 1031-1035, 1032t Bad Ad program, 594, 1011, 1012, 1025, 1030-1031 case studies, 1024, 1030, 1031, 1034 conclusion, 1035 defined, 1012 DTCA defined, 1014 evaluating, 1023-1025, 1023t-1024t, 1024t-1025t help-seeking ads, 1012, 1015-1018, 1024, 1025t improvement suggestions, 1022-1023 information not required for inclusion, 1016t legal aspects of, 524-526, 1017-1018 opposition, 1020-1022 PhRMA guidelines for, 1018, 1019t product claim ads, 1012, 1015-1018, 1024t reminder ads, 1012, 1015-1018, 1025t support, 1018-1020 types, 1015-1016 to HPs, 1025-1035 legal issues, 1026 misinformation, 1027-1030, 1029t promotion effectiveness, 1026-1027 impact of, 1013 introduction, 1012-1013 self-assessment questions, 1036-1038

1285

Drug repository programs, 919 Drug safety. See Medication and patient safety; Pharmacovigilance Drug Safety Communication (DSC), 973, 990, 992, 996t, 997t, 1008 Drug substance, 832 Drug therapy, genetic information and. See Pharmacogenomics Drug Topics Red Book, 63t, 68 Drug use evaluation (DUE), 679-680 Drug use review (DUR), 23, 614, 691, 719, 720t, 729 DrugCite, 750 DRUGDEX, 529, 530 Drugs in Pregnancy and Lactation (Brigg’s), 64t, 75 Drugs.com, 63t, 72 Drugsite Trust, 72 DSC. See Drug Safety Communication DSs. See Dietary supplements DTCA. See Direct-to-consumer advertising DUE. See Drug use evaluation Due care, 506, 509, 518, 537 DUR. See Drug use review Durham-Humphrey Amendment, 834-835 Duty breached, 506, 507 deontological theories, 574, 575, 579, 580, 581, 585, 586, 589 duty of care, 506-507, 510-511 legal, 507 DV. See Dependent variable

E EACA. See Epsilon-aminocaproic acid EBM. See Evidence-based medicine EBSCOhost, 86 Echinacea, 252, 753 Economic, clinical, and humanistic consequences of health care processes (ECHO model), 709, 736 Economic analysis, pharmacoeconomics. See Pharmacoeconomic analysis Eddy, David, 315 Editing, of professional writing projects, 469 Editor, letters to, 167-168 Editorials, on clinical trials, 106, 165-167 Education. See also Community pharmacy practice; Drug information education and training; Medical education FDA, ACCME, PhRMA guidelines, 504, 551-552, 553t medication errors and, 810-811 pharmaceutical industry support, 550-554, 552t, 553t Effect size clinical significance and, 391-3932 sample size and, 132-134

1286

INDEX

Effectiveness, of drug promotions, 1026-1027 Efficacy and Safety of Subcutaneous Enoxaparin in Non-Q-Wave Coronary Events (ESSENCE) trial, 130 Electronic medical records (EMRs), 712, 757, 785, 855, 1052, 1054, 1061 Electronic medication administration records (eMars), 1047, 1048, 1058 Electronic prescribing (e-prescribing) platforms, 908, 1045, 1047, 1048, 1051-1053 Elements to assure safe use (ETASU), 848, 991t Eliquis®. See Apixaban E-mail, liability and, 533 eMARs. See Electronic medical administration records EMBASE, 63t, 64t, 86, 108t, 237, 254, 324, 508, 910 Employer defenses, to negligence, 520 EMRs. See Electronic medical records Enalapril, 46-47 Encyclopedia of Wild Mushrooms, 515 EndNote software, 474 Endnotes, in professional writing projects, 460, 468, 471, 474 Endometrial cancer, coffee consumption, 220 Endpoints composite, 129-130, 148, 166 controlled clinical trials, 129-131, 145-146 primary, 129-131 surrogate, 131, 146, 329, 685, 847 Enoxaparin, 130, 295, 507 EORTC QLQ-C30. See European Organization for Research and Treatment of Cancer Ephedra, 762 Ephedrine, 749 Epidemiological statistics, 370-374 incidence and prevalence, 371 overview, 370 ratios, proportions, and rates, 370-371 relative risk and odds ratio, 371-373 sensitivity, specificity, and predictive values, 373-374 Epinephrine, 754t, 794t Epocrates, 67, 82, 532, 907 E-prescribing platforms. See Electronic prescribing platforms Epsilon-aminocaproic acid (EACA), 399-400 Equivalency, no difference and, 160-161 Erectile dysfunction, 426, 1037 Error reporting systems. See also Adverse drug events; Adverse drug reactions; Medication errors managing, 793-795 Medication Error and Adverse Drug Event Reporting System, 756 VA Adverse Drug Event Reporting System, 757 Vaccine Adverse Event Reporting System, 757, 764, 770

Errors. See also Biases; Medication errors; specific errors abbreviations, 646 chemotherapy, 803 coverage, 229 discussion question, 802 illegible handwriting, 646 measurement, 229-230 medical errors, 778, 782, 820, 1046, 1062 nonresponse, 230 sampling, 229 statistical power and, 386-387 statistical significance and, 150-152, 151f survey research, 229-230 transcription, 646 Type I, 139-142, 140t, 386-387 Type II, 139-142, 140t, 386-387 Erythromycin, 170-172, 173, 174, 508, 514 Esomeprazole (Nexium), 120, 153-154, 615, 910 ESSENCE trial. See Efficacy and Safety of Subcutaneous Enoxaparin in Non-Q-Wave Coronary Events Essentials of Pharmacoeconomics, 302 ETASU. See Elements to assure safe use Ethical covenant, 581 Ethical decision making macro level, 569, 571, 581, 584, 589, 591, 592, 593, 596, 598, 599, 601 meso level, 569, 571, 572, 581, 584, 587, 589, 590, 591, 592, 593, 594, 596, 598, 599, 601 micro level, 569, 571, 572, 582, 584, 590, 591, 592, 593, 598, 599, 601 support structures for, 595-597 Ethical theory, 574, 574f, 575, 576, 578, 579-580 Ethics, 569-605 annotated listing of rules and principles, 580-582 case studies, 582-592 conclusion, 597 cultures and, 577 definitions used in, 574-575 description of, 570-572 drug promotion, WHO ethical criteria, 1013-1014 examples of dilemmas, 573t law compared with, 571-572 overview, 572-573 Principles of Biomedical Ethics, 572, 605 resources for, 592-595 self-assessment questions, 598-601 suggested process of analysis ethical theory usage, 579-580 identification of relevant background information, 576-577 using rules and principles, 578-579 suggested readings, 605 WHO criteria for drug promotion, 1013-1014 Ethics and Information Technology: A Case-Based Approach to a Health Care System in Transition, 594

INDEX

Ethics Course Content Committee, 570 European Organization for Research and Treatment of Cancer (EORTC QLQ-C30), 247 European Union 1996 Database Directive, 545 Evaluation of Corlopam in Patients at Risk for Renal Failure-A Safety and Efficacy Trial (CONTRAST study), 134 Evaluation of drug literature. See Controlled clinical trials; Study designs Evaluation of drugs. See Drug evaluation monographs Evaluation of DTCA. See Direct-to-consumer advertising Evaluation of Genomic Applications in Practice and Prevention, 685 Evaluation of medication use. See Medication use evaluation Event reporting systems. See also Adverse drug events; Adverse drug reactions; Medication errors managing, 793-795 Medication Error and Adverse Drug Event Reporting System, 756 VA Adverse Drug Event Reporting System, 757 Vaccine Adverse Event Reporting System, 757, 764, 770 Everyday Health, 936t Evidence admissible, 323-324 levels of, 213, 249f, 257, 686t, 688t, 877f professional writing and, 468 Evidence-based clinical practice guidelines, 311-350. See also Evidence-based medicine AGREE II instrument, 312, 334-336, 335t ambulatory care clinicians and, 908-911 attraction of, 313 case studies, 320, 325, 334, 340 clinical practice guidelines consensus-based, 245-246 EBM and, 245-246, 314-316 introduction, 312-314 purpose, 246 conclusion, 342-343 defined, 312-313 development methods, 316-334 evaluation tools, 312, 334-336 GRADE system, 312, 326-331, 328t, 332 health care system and, 313-314 implementation, 312, 336-340 barriers, 337-338, 338t, 340 CDSSs, 339 introduction, 312-314 IOM standards articulate recommendations, 318t, 331-333 establish transparency, 317t, 319 evidence foundations and rating strength of recommendations, 318t, 326-331

1287

external review, 318t, 333 list, 317t-318t manage conflict of interest, 317t, 319 multidisciplinary guideline development groups, 317t, 320 overview, 316, 319 plan for updating guidelines, 318t, 334 systematic review, 317t, 320-325 P&T committees, 642 purpose, 246 self-assessment questions, 343-346 sources of, 312, 341-342 study designs and, 245-246 suggested readings, 349-350 Evidence-based decisions, 588, 630 Evidence-based medicine (EBM). See also Evidence-based clinical practice guidelines clinical practice guidelines and, 245-246, 314-316 Cochrane Library, 86, 244, 324, 341 McMaster University and, 314-315 medication information provision and, 13-17 The Pharmacist’s Guide to Evidence-Based Medicine for Clinical Decision Making, 263, 272 Evidence-Based Medicine Working Group, 314-315 Excel, decision analysis with, 295 Exclusion criteria, for controlled clinical trial, 118-121 Exculpatory clauses, 523 Exenatide therapy, 423, 424 Exotic Animal Formulary, 64t Experimental designs, 374-375. See also Controlled clinical trials; Randomized controlled trials Expert judgment/global introspection, ADR causality, 746 Expiration, patents, 632, 633, 1015 Explanatory surveys, 228 Exponential distribution, 368t Exposure suspicion bias, 207f, 214t Extemporaneous Formulations (Children’s Hospital of Philadelphia), 68 External review, evidence-based clinical practice guidelines, 318t, 333 External validity, 117t, 118, 120, 149, 151, 254, 255, 325, 725t F FACT. See Functional Assessment of Cancer Therapy Factorial ANOVA with repeated measures. See Mixed between-within ANOVA Factorial between-groups ANOVA, 395t, 410-412, 420, 421, 422 factsandcomparisons.com, 65, 70, 71, 912

1288

INDEX

Factual data, survey research, 231 Failure analysis. See Survival analysis Failure mode and effects analysis (FMEA), 778, 795, 797, 817 Failure to warn, 504, 508, 509, 523, 525, 527. See also Learned intermediary rule Fair use, 536, 539-542, 544, 545. See also Copyright False negatives (FN), 139, 140, 141, 373, 374, 386, 387 False positives (FP), 139, 141, 147, 373-374, 384, 386, 387 Family history bias, 206f Fast Track review, 685, 847 FBOLs. See Field-based outcomes liaisons FDA. See Food and Drug Administration FDAAA. See Food and Drug Administration Amendments Act FDAMA. See Food and Drug Administration Modernization Act FDC Reports, 92t FDCA. See Food, Drug & Cosmetic Act FDS. See Formulary decision supports Federal agencies, pharmaceutical industry and, 986-993 Fee-for-service. See Contract drug information centers Female sexual arousal disorder, 88-89 Fenfluramine, 233 Fenoldopam mesylate, 133-134 Fictitious reporting, 545. See also Plagiarism Fidelity, 581, 585, 586f Fidelity Leasing Corp. v. Dun & Bradstreet, Inc., 521 Field-based outcomes liaisons (FBOLs), 976 Find, Organize, Clarify, Understand, Select-PDCA (FOCUS-PDCA), 704, 704f, 707-708, 720, 735 Fiorinal, 509 First Amendment, 515, 516, 526, 1026 Fisher’s exact test, 396t, 401, 402-403, 415, 425 Fixed cohort, 216 Flavanol-rich cocoa products, blood pressure and, 249-250 Flawed studies, statistical analysis and, 450-451 FLIC. See Functional Living Index-Cancer Flipping coins, 367, 406, 858 Flumazenil, 754t, 794t Fluoroquinolone antibiotics, 289, 527 FMEA. See Failure mode and effects analysis FN. See False negatives FOCUS-PDCA. See Find, Organize, Clarify, Understand, Select-PDCA Follow-up in clinical trials, 131-132 for MUE, 732 responses to drug information queries, 45 Follow-up studies, 220. See also Cohort studies Food, Drug & Cosmetic Act (FDCA), 834

Food and Drug Administration (FDA). See also Division of Drug Information; MedWatch program; Pharmaceutical industry ADRs prevention, 744t reporting to FDA, 759-761 advisory committees, 689, 845 Bad Ad program, 594, 1011, 1012, 1025, 1030-1031 bioequivalence regulations, 210-212 Chemical Type classification, 617, 617t CHI recommendations, 94t Critical Path Initiative, 203, 836 CVM homepage, 79 defined, 833 dietary supplements and, 255 drug evaluation monograph summary page, FDA classifications, 673, 674-675, 674t, 675t DTCA evaluation, 1024t-1025t educational policies, communication between industry and CME providers, 504, 551-552, 553t FDAAA, 165, 761, 835, 836, 990, 1017 FDAMA, 503, 523, 527, 835-836 goal of, 835 Good Reprint Practices, 527-528, 528t guidance documents, 979 Help-Seeking and Other Disease Awareness Communications guidelines, 531 investigational drugs and, 830, 831 mission of, 988 NI studies and, 192 NIH-FDA joint venture, 685 Office for Generic Drugs, 210 off-label use and, 527-529, 528t opportunities within, 1000-1002 patent information from, 79 pharmaceutical industry communication of drug safety information, 988 drug information resources, 995, 996t-997t, 998 opportunities within, 1000-1002 organization of offices and centers, 989f regulation by, 978-980 safety communication tools, 988, 990 pharmaceutical industry regulation, 978-980 pharmacogenomics and, 685 quality of health information checklists, 944 stability testing guidelines, 209 Therapeutic Potential classifications, 617, 617t Therapeutic Rating classification, 674, 675t VAERS program, 764 WebMD®-FDA joint online venture, 93 Food and Drug Administration Amendments Act (FDAAA), 165, 761, 835, 836, 990, 1017 Food and Drug Administration Modernization Act (FDAMA), 503, 523, 527, 835-836

INDEX

Food-Medication Interactions, 63t, 71 Ford, Mrs. Billings example, 582-587 Foreign drug identification, 71-72 Foreign patients, 121 Forest plots, 242-243, 243f, 245 Formal consensus-based practice guidelines, 244-245 Formularies (drug formularies). See also Drug evaluation monographs; Pharmacy and Therapeutics committees ambulatory care clinician and, 905-908 closed versus open, 633-635 drug evaluation for, 629-633 drug evaluation monographs and, 670 formulary system for, 625-626 generic substitution, 637 historical background, 609-610 new product introductions, 638-639 nonformulary usage, 637-638 open versus closed, 633-635 patient pocket formularies, 629 PBMs and, 629, 632, 634, 650 rights v. privileges with, 644 sections of, 628 strictness of, 626, 627 therapeutic interchange, 614, 635-637 unlabeled uses, 638 Formulary committees, 608-609 Formulary consideration form, 618, 619t Formulary decision supports (FDS), 908 Formulary system, 625-626 Formulating effective responses and recommendations. See Responses-recommendations Formulations, 63t, 68-69. See also Compounding FP. See False positives Frame, sample, 229, 231-232 Framing, 322, 323, 344, 537 Fraud, on Internet, 535-537 Friedman two-way ANOVA by ranks, 395t, 424 Frye v. Medicare-Glaser Corporation, 509 Functional Assessment of Cancer Therapy (FACT), 247 Functional Living Index-Cancer (FLIC), 247 Funding, of clinical trials, 110t, 163-165 Funnel plots, 240, 240f, 244, 329, 330

G GABA Plus, 90 GADIS. See Global Alliance of Drug Information Specialists Galley proofs, 476 Gamble method, standard, 285-286 Gamma distribution, 368t Gantt chart, 887, 889, 890, 891 GAO reports. See General Accounting Office reports

1289

Gastroesophageal reflux disease (GERD), 54, 56, 198, 262, 282, 615, 910, 1034 Gaussian distribution. See Normal distribution Gendor bias, 120 General Accounting Office (GAO) reports, 538, 1014 General product information resources, 63t, 65-67 Generic drugs, bioequivalence trials on, 190t, 210-212 Generic instruments, HR-QOL measurements, 247-248 Generic substitution, 637 Genetic information, drug therapy and. See Pharmacogenomics Genome-wide association studies, ADRs and, 766 GERD. See Gastroesophageal reflux disease Geriatric Dosage Handbook, 63t, 71 Geriatric dosage recommendations, 63t, 71 Gettingwell.com, 94t Ghostwriting, 463, 514, 554 G-I-N. See Guidelines International Network Ginkgo, 85, 254 Glimepiride, 200 Global Alliance of Drug Information Specialists (GADIS), 994t, 995t, 997t, 999 Global introspection/expert judgment, ADR causality, 746 GlucoseBuddy, 939t Glycosylated hemoglobin, 355, 709 Gold Standard, clinicalpharmacology.com, 63t, 64t, 65, 72, 94, 529, 530, 911, 912-913 Goldfrank’s Toxicologic Emergencies, 64t, 77 Goldman’s Cecil Medicine, 64t, 76 Gompertz distribution, 368t Good clinical practice, 833 Good Reprint Practices, 527-528, 528t Goodman & Gilman’s: The Pharmacological Basis of Therapeutics, 64t, 74 Goodness-of-fit test, 407. See also Kolmogorov-Smirnov one-sample test Google Health, 936t Google Library Project, 544 Google Scholar, 87, 90, 531, 532, 533 Google searches, ambulatory care drug information, 902 gpoaccess.gov/cfr/index.html, 75, 837, 869. See also Code of Federal Regulations Grades of Recommendation Assessment Development and Evaluation (GRADE), 312, 326-331, 328t, 332. See also Evidencebased clinical practice guidelines Grammar and spelling, professional writing, 466t, 467 Graphical representations, descriptive statistics, 359f, 361f, 362-363, 363f Green Book, 79 Greenmoss Builders v. Dun & Bradstreet, 516 Group comparison design, 378

1290

INDEX

Group sequential trial, 203. See also Adaptive clinical trials Guanfacine, 84, 84f Guide to Federal Pharmacy Law, 64t Guidelines International Network (G-I-N), 342

H H0. See Null hypothesis H1. See Research hypothesis Hand v. Krakowski, 508 Handbook of Injectable Drugs, 63t, 72 Handbook of Nonprescription Drugs: An Interactive Approach to Self-Care, 63t, 66 Hansten and Horn’s Drug Interaction Analysis and Management, 63t, 70-71 Haphazard approach, to drug information queries, 36, 43. See also Drug information queries Happel v. Wal-Mart Stores, 510 Harbeson v. Parke Davis, 518-519 Harms and benefits, of health care interventions, 312-314. See also Evidencebased clinical practice guidelines Harper & Row Publishers, Inc. v. Nation Enterprises, 539 The Harriet Lane Handbook, 64t, 73 Harrison’s Principles of Internal Medicine, 64t, 76 Harvard Medical Practice Study I, 782 Hazard ratio (HR), 159-160 HC approach. See Human capital approach HCTZ. See Hydrochlorothiazide Health 2.0, 1060-1061. See also Web 2.0 Health and Human Services (HHS, Department of Health and Human Services). See also Centers for Disease Control and Prevention; National Institutes of Health healthfinder.gov, 94t, 935t list of agencies and offices, 986t-987t Health care. See also Pharmacoeconomics; Quality improvement ADRs’ impact on, 743 Affordable Care Act, 13, 274, 302 changes changing environment for health care quality, 704-705 factors influencing, 2 new technologies and drugs, 274 evidence-based clinical practice guidelines and, 313-314 IHI and, 780t, 783t, 785, 811, 812t medication errors’ impact on, 782-783 Methods for the Economic Evaluation of Health Care Programmes, 302 patient-centered, 5, 14, 778, 810, 818, 921, 931, 964, 1046, 1060 pharmacy informatics, 1060-1061

transparency, 703, 704-705, 734, 1063 value and, 703 value-driven, 704, 734 Health care professionals (HPs). See also Drug information specialists; Drug promotions; Pharmaceutical industry; Pharmacists drug promotions to, 1025-1035 legal issues, 1026 misinformation, 1027-1030, 1029t promotion effectiveness, 1026-1027 investigational drug role of, 852-859 Health care quality. See Quality improvement Health Evaluation through Logical Processing, 756 Health information. See also Consumer health information communication privacy, 549-550 HIPAA, 535, 546-549, 550, 1065 Health information technology (HIT) pharmacist’s role, 10-13 pharmacy informatics and, 1047-1048 Health Insurance Portability and Accountability Act of 1996 (HIPAA), 535, 546-549, 550, 1065 Health literacy, 945-946, 946t Health literate Internet sites, 946t Health maintenance organizations (HMOs) closed formularies, 634 defined, 626-627 medication information specialist and, 19, 21 NCQA and, 627 Health on the Net (HON), 11, 91, 481, 532, 944 Health outcomes research. See also Pharmacoeconomics defined, 246 HR-QOL, 188, 247-252, 285, 709 QOL measures, 190t, 246-252, 249f Health Professions Education: A Bridge to Quality, 810 Health professions education, medication errors, 810-811 Health Resources and Services Administration (HRSA), 783t, 921 922, 987t Health Services Technology Assessment Texts (HSTAT), 342, 909 Health status assessments, HR-QOL measurements, 247 Health Summit Working Group, 532 Health system policies. See Policies Health system policy development. See Policy development Health system projects. See Projects Healthfinder.gov, 94t, 935t Health-related quality of life (HR-QOL), 188, 247-252, 285, 709 Health-system policy. See Policy Heartburn, 198, 910 Help-seeking advertisements, 1012, 1015-1018, 1024, 1025t

INDEX

Help-Seeking and Other Disease Awareness Communications by or on Behalf of Drug and Device Firms and Brief Summary: Disclosing Risk Information in ConsumerDirected Print Advertisements, 531 Hemoglobin, 9, 49, 131, 143, 146, 200, 355, 400, 709 Heparin, 55, 56-57, 129, 130 Heparin-induced thrombocytopenia (HIT), 56-57, 785 Herbal medicine. See also Complementary and alternative medicine; Dietary supplements PDR for Herbal Medicines, 63t, 66, 70 resources on, 17, 94t Heredia v. Johnson, 511 HESDE. See Historical evidence of sensitivity to drug effects Heterogeneity, meta-analyses and, 239, 241, 242, 243f, 245 HHS. See Health and Human Services Hip replacement surgery, 295, 414 HIPAA. See Health Insurance Portability and Accountability Act of 1996 Hippocratic Oaths, 579-580 Histograms, 136f, 137f, 358, 359f, 362 Historical cohort study. See Retrospective cohort study Historical control, 122, 123, 832 Historical evidence of sensitivity to drug effects (HESDE), 196 HIT. See Health information technology; Heparin-induced thrombocytopenia HIV. See Human immunodeficiency virus. HMG-CoA reductase inhibitors. See Hydroxymethylglutaryl-Co A reductase inhibitors HMOs. See Health maintenance organizations Homogeneity meta-analyses and, 234, 241, 242, 244, 245 of regression, 413 of variance, 408, 410, 411, 412, 413, 417, 418, 420, 422, 423 Homoscedasticity, 430, 433, 436 HON. See Health on the Net HONcode, 481, 532 Horner v. Spalitto, 510 Hospital Corporation of America, 707 Hospital organization, 611, 611f Hospital Pharmacy: What Is Ethical? article, 575 HPs. See Health care professionals HR. See Hazard ratio HR-QOL. See Health-related quality of life HRSA. See Health Resources and Services Administration HSTAT. See Health Services Technology Assessment Texts Human capital (HC) approach, 274, 280 Human factors principles, 815-816

1291

Human immunodeficiency virus (HIV), 51, 52, 251-252, 549, 1033. See also Acquired immunodeficiency syndrome Human practice guidelines, NLM, 107 Hydrochlorothiazide (HCTZ), 155-156 Hydromorphone hydrochloride extendedrelease capsules (Palladone Extended-Release), 233, 918t Hydroxydaunomycin, 48, 287, 813 Hydroxymethylglutaryl-Co A (HMG-CoA) reductase inhibitors, 123, 129, 238, 527, 636, 675, 893. See also Low-density lipoprotein-cholesterol Hypereosinophilic syndrome, 510 Hyperlinking, 531, 536, 545, 911, 912 Hyperlipidemia, 373, 417 Hypersensitivity, ADR classification, 752 Hypertension antihypertensive therapies, 51, 52, 53, 133, 153, 161, 194, 196, 248, 257 case study, 51-53 Hypotheses. See also Statistical analysis null, 115, 139, 195, 195f, 198, 200, 386-388, 387t null hypothesis significance testing, 386, 387t one-tailed hypothesis tests, 138, 139, 386, 388, 400 research, 115, 116, 134, 139, 152, 195, 195f, 198, 200, 386, 388 two-tailed hypothesis tests, 138, 139, 168, 386, 388, 400

I ICD. See International Classifications of Diseases ICER. See Incremental cost-effectiveness ratio ICH. See International Conference on Harmonization ICMJE. See International Committee of Medical Journal Editors Ident-a-Drug, 63t, 71-72 Identification. See Adverse drug events; Medication errors Idiosyncrasy, ADR classification, 752 IDIS. See Iowa Drug Information Service IDMC. See Independent data monitoring group/committee iFacts, 82 IHI. See Institute for Healthcare Improvement Illegible handwriting, P&T committees and, 646 ILLUMINATE trial, 146 Immunization information, drug information in ambulatory care, 919-920 Implementation, of clinical practice guidelines. See Evidence-based clinical practice guidelines Implementation packet, 890 Import Drug Act of 1848, 834

1292

INDEX

IMRAD. See Introduction, Methods, Results, and Discussion In re Michael A. Gabert, 513 In re Pharmatrak Inc. v. Privacy Litigation, 550 In vitro companion diagnostic device, 838 In vitro studies, 46, 47, 209-210, 252, 254, 255, 837, 840, 843 Incidence, epidemiological statistics, 371 Incidence study. See Cohort studies Inclusion criteria, for controlled clinical trial, 118-121 Incompatibility, resources on, 63t, 72 Incomplete information, liability for, 509-511 Incorrect dosage form error, 789 Incorrect dose error, 647, 781, 788 Incorrect duration error, 789 Incorrect medication error, 788, 813, 882 Incorrect patient action error, 789 Incorrect patient error, 788 Incorrect preparation error, 789 Incorrect rate error, 788 Incorrect route of administration error, 788 Incorrect strength or concentration error, 789 Incorrect timing error, 788 Incremental cost-effectiveness ratio (ICER), 283, 284, 284t, 295t, 300 Incremental cost-utility ratio, 287 IND. See Investigational New Drug Application Independent data monitoring group/committee (IDMC), 198-199, 205, 208 Independent variable (IV), 355 Independent-samples t test (Student’s t-test), 135, 138, 381, 384, 391, 395t, 400, 407-408, 409, 414 Index Nominum: International Drug Directory, 64t, 72-73 Indexing services BIOSIS Previews, 64t, 85 CANCERLIT, 86 CINAHL, 86, 324, 876 Cochrane Library, 86, 244, 324, 341 Current Contents Connect, 63t, 86, 324 described, 60, 83, 108t EMBASE, 63t, 64t, 86, 108t, 237, 254, 324, 508, 910 Google Scholar, 87, 90, 531, 532, 533 IDIS, 63t, 64t, 83, 87, 237, 877, 909 IPA, 63t, 64t, 87, 108t, 114, 237, 876 Journal Watch, 87, 167 LexisNexis, 63t, 64t, 88, 91, 108t MEDLINE, 5, 63t, 64t, 86, 88, 108t, 237, 254, 324, 341, 349, 508, 532, 543, 910 Pharmacoeconomics & Outcomes News Weekly, 88 PubMed, 11, 88, 108t, 114, 166, 237, 254, 508, 532, 876, 909, 910 Reactions Weekly, 63t, 64t, 88, 751 Indicator drugs, ADR, 754t Indicators. See Performance indicators Indirect costs, 276, 277

Individual defenses, to negligence, 518-519 Infectious disease, ad hoc P&T committee for, 620-621 Inferential statistics, 384-393 central limit theorem, 352, 385-386, 393, 397, 405, 406, 408, 409, 411, 413, 417, 420, 422 controlled clinical trials, 135-138 descriptive statistics compared to, 357 hypothesis testing, 386 overview, 384-385 parametric versus nonparametric testing, 392-393 quasi-experimental designs, 374, 375-376 sampling distributions, 385-386 statistical power, 386-387 statistical versus clinical significance, 387-392 Type I and II errors, 139-142, 140t, 386-387 Influenza vaccination, 371, 425-426, 512, 814 Informatics. See also Pharmacy informatics drug, 3, 20 medical, 20 specialist, 20 Information. See also Background information; Drug information; Medication information inappropriate analysis or dissemination of, 517-518 inappropriate quality information, 513-517 incomplete, 509-511 quality of information, Web 2.0, 531-533 security communication privacy, 549-550 HIPAA, 535, 546-549, 550, 1065 Information bias, 206t, 219 Information retrieval international DS trials and, 253-254 pharmacy students, 7, 955, 956, 960 Information technology, 2, 12-13. See also Health information technology; Informatics; Internet Informed consent of clinical trial subjects, 124-125 off-label use and, 527-530, 528t Injectable drugs errors, dosage form, 807 policies and procedures, 640-641 Trissel’s Handbook of Injectable Drugs, 63t, 72 Institute for Healthcare Improvement (IHI), 780t, 783t, 785, 811, 812t Institute for Safe Medication Practices (ISMP), 690, 744t, 793, 795, 811, 814, 819t, 922. See also Medication and patient safety Institute of Health Economics, 302t Institute of Medicine (IOM). See also Evidencebased clinical practice guidelines standards for developing evidence-based clinical practice guidelines, 317t-318t articulate recommendations, 318t, 331-333 establish transparency, 317t, 319

INDEX

evidence foundations and rating strength of recommendations, 318t, 326-331 external review, 318t, 333 manage conflict of interest, 317t, 319 multidisciplinary guideline development groups, 317t, 320 overview, 316, 319 plan for updating guidelines, 318t, 334 systematic review, 317t, 320-325 Institutional Review Board (IRB) defined, 124, 833 described, 830, 849-851 experimental drugs, 529 P&T committee functions and, 624, 650 subject consent, 124-125 Instrument reliability, survey research, 232 Intangible costs, 273, 276, 277, 279 Intellectual property rights. See Copyright Intention-to-treat (ITT) analysis, 144-145, 168, 197, 256, 380 Interactions. See Drug interactions Interim analyses, 132, 199, 208, 379, 384 Internal audit. See Quality improvement Internal validity, 116, 117t, 149, 151, 231, 255, 325, 1034 International Classification of Diseases (ICD), 83, 755, 1062 International Committee of Medical Journal Editors (ICMJE), 109, 164, 469 International Conference on Harmonization (ICH), 209, 836, 869 International drug equivalency, 64t, 72-73 International Foundation for Functional Gastrointestinal Disorders, 92 International Journal of Pharmacy Compounding, 68 International Pharmaceutical Abstracts (IPA), 63t, 64t, 87, 108t, 114, 237, 876 International Society for Pharmacoeconomics and Outcomes Research, 302t International trials, on dietary supplements, 253-254 Internet. See also Community pharmacy practice CHI on, 93-94, 94t clinical trial recruitment on, 120-121 HON Foundation, 11, 91, 481, 532, 944 liability concerns for fraud and abuse, 535-537 telemedicine and cybermedicine, 533-535 news sources, 91, 92t searches, drug information resources, 90-93, 92t unreliable drug information, 112-113 Internet Healthcare Coalition, 532 Interquartile range (IQR), 135t, 136-138 Interval data, 134, 135t Interval scale, 356 Interval variables. See Continuous variables Intervention group, 121-124

1293

Interventional trials, 188, 213. See also Controlled clinical trials; Randomized controlled trials Interventions, for MUE, 731-732 Intolerance, ADR classification, 752 Intravenous antibiotic example, costminimization analysis, 278-279 Introduction, Methods, Results, and Discussion (IMRAD), 469 Introduction section controlled clinical trials, 110t, 115-116 of professional writing projects, 470 Introductory Pharmacy Practice Experiences (IPPEs), 24, 958 Inverse Gaussian distribution, 368t Inverse Variance test, 242 Investigational agent data sheet, 855 Investigational device exemption, 838 Investigational drugs, 829-869 case studies, 844, 848, 851-852 conclusion, 859-860 definitions of terms, 832-833 drug approval process, 837-848 drug development regulation history, 834-837 FDA and, 830, 831 Institutional Review Board, 830, 849-851 introduction, 831 orphan drugs, 617t, 675t, 829, 830, 848-849 self-assessment questions, 860-862 suggested readings, 869 Investigational New Drug Application (IND) defined, 832, 833 drug approval process and, 837-842 Investigators controlled clinical trials, 111-113 for investigational drugs, 833 IOM. See Institute of Medicine Iowa Drug Information Service (IDIS), 63t, 64t, 83, 87, 237, 877, 909 IPA. See International Pharmaceutical Abstracts IPPEs. See Introductory Pharmacy Practice Experiences IQR. See Interquartile range IRB. See Institutional Review Board Ischemic stroke, 159, 160, 205, 323, 814 ISMP. See Institute for Safe Medication Practices iTriage, 939t ITT analysis. See Intention-to-treat analysis IV. See Independent variable

J JAMA. See Journal of the American Medical Association JCAH. See Joint Commission on Accreditation of Hospitals JCAHO See Joint Commission for the Accreditation of Healthcare Organizations

1294

INDEX

JCPP. See Joint Commission of Pharmacy Practitioners Jeppesen case, 516 Joint arthroplasty, 399-400 Joint Commission, The. See The Joint Commission Joint Commission Accreditation Process Guide for Hospitals, 621, 631-632, 648 Joint Commission for the Accreditation of Health Care Organizations (JCAHO), 609 Joint Commission of Pharmacy Practitioners (JCPP), 947 Joint Commission on Accreditation of Hospitals (JCAH), 610 Joint decision points, 293 Journal of the American Board of Family Medicine survey, 916 Journal of the American Medical Association (JAMA), 109, 464, 744 Journal Watch, 87, 167 Journals. See also Primary literature controlled clinical trials and, 111-113 secondary, 105, 167 supplements, 112 Judeo-Christian tradition, 579 Just Culture, 802-806 Justice principle, 581-582, 585, 586f

K Kaplan-Meier method, 441, 442, 443-445, 443f, 444f, 446, 447, 448, 449 Kappa statistic (κ), 396t, 403-404 Keep It Simple, Stupid (KISS), 467 Kefauver-Harris Amendment of 1962, 759, 835, 1017 Ketorolac, 510 King Guide to Parenteral Admixtures, 63t, 72 KISS. See Keep It Simple, Stupid Knowledge-based errors, 777, 778, 798-800, 804 Known Adverse Effects/Toxicities section, of drug evaluation monograph, 690 Kolmogorov-Smirnov one-sample test, 395t, 407 Kruskal-Wallis one-way ANOVA by ranks, 395t, 415-416 Kurtosis, 360, 362, 365, 392

L Labeling, legal aspects of doctrine of drug overpromotion, 526-527 DTC drug information, 524-526 erosion of learned intermediary rule, 504, 506, 510, 523, 524-526 off-label use and informed consent, 527-530 Labetalol, 51, 52, 53 Lactation drug safety, 519 Drugs in Pregnancy and Lactation, 64t, 75

Language bias, 238 Lansoprazole, 153-154, 162, 1021 Large, simple trials (LST design), 765 Last observation carried forward (LOCF) technique, 352, 380 Law. See also Tort law ethics compared with, 571-572 resources on pharmacy law, 64t, 75 Lawsuits, poison information centers, 517-518 LDL-cholesterol. See Low-density lipoprotein-cholesterol LDLs. See Low-density lipoproteins Leadership and career opportunities. See Drug information specialists Lead-in phase. See Run-in phase Leapfrog Group, 783t Learned intermediary rule, 504, 506, 510, 523, 524-526 Learning objectives, for presentations, 485-486 Legal aspects, of drug information practice, 503-568. See also Copyright; Court cases; Liability advertising and labeling, 523-530 case studies, 507, 508-509, 540, 548 conclusion, 554-555 defenses to negligence, 518-520 DTCA, 524-526, 1017-1018 industry support for educational activities, 550-554, 552t, 553t intellectual property rights, 537-546 introduction, 504-505 labeling and advertising, 523-530 malpractice protection, 512, 520-523, 522t privacy communication, 549-550 HIPAA, 535, 546-549, 550, 1065 self-assessment questions, 555-559 suggested readings, 568 tort law, 505-518 Legal duty, 07 Leptokurtic distribution, 362 Lethal, ADR classification, 751 Letter of Authorization (LOA), 841 Letters to editor, clinical trials, 167-168 Levalbuterol, 114 Levels of evidence, 213, 249f, 257, 686t, 688t, 877f Levofloxacin, 75-76 Lexicomp CHI, 94 drug information database, 63t, 64t, 82, 908 Drug Information Handbook, 66, 912 PDA drug information database, 82 as tertiary resource, 108t Lexicomp Online, 72, 912-913 Lexi-Interact, 82 LexisNexis, 63t, 64t, 88, 91, 108t Liability civil, 520 defenses to negligence

INDEX

employer defenses, 520 individual defenses, 518-519 malpractice protection, 512, 520-523, 522t drug information specialists and pharmacists for inappropriate analysis or dissemination of information, 517-518 for inappropriate quality information, 513-517 for incomplete information, 509-511 for negligence and failure to warn, 504, 508, 509, 523, 525, 527 labeling and advertising aspects of doctrine of drug overpromotion, 526-527 DTC drug information, 524-526 erosion of learned intermediary rule, 504, 506, 510, 523, 524-526 off-label use and informed consent, 527-530 quality assurance and, 521, 522t strict, 511, 514-515, 516 tort law, 505-518 vicarious, 512, 519, 556 Web 2.0 information fraud and abuse, 535-537 quality of information, 531-533 social media, 535 telemedicine and cybermedicine, 533-535 Libertelli v. Hoffman La Roche, Inc. & Medical Economics Co., 515 Libraries Cochrane Library, 86, 244, 324, 341 Google Library Project, 544 local, CHI and, 93 National Library of Medicine CHI, 94t HSTAT Web site, 342, 909 human practice guidelines, articles, 107 indexing service, 83 Loansome Doc, 90 MedLARS, 5 MEDLINE, 5, 63t, 64t, 86, 88, 108t, 237, 254, 324, 341, 349, 508, 532, 543, 910 MedlinePlus, 63t, 91, 94t, 943, 944, 954t RxNorm, 1062 as secondary resource, 108t video, on evaluating Web sites, 91, 944 National Medical Library, 541 Life tables, 215, 441, 442, 443 Likert-type scale, 135t, 414, 416 Linagliptin, 200 Linear regression multiple, 396t, 399, 412, 428, 433-436, 437, 440, 446 simple, 394, 396f, 397, 430-433, 434, 435 Lipitor. See Atorvastatin Listservs, 877, 878, 995t, 998 Literature evaluation. See Controlled clinical trials; Study designs LOA. See Letter of Authorization Loansome Doc, 90 Locality rule, 506

1295

LOCF technique. See Last observation carried forward technique Logical fallacies, drug promotion, 1029t Logistic regression, 381, 384, 391, 396t, 437-441, 442, 446, 448, 454 Logits, 439-440, 441 Log-odds, 439-440, 441 Log-rank tests, 445-446 Longitudinal study. See Cohort studies Lorazepam, 51, 52 Lorcaserin, 168-169 Losartan, 129 Lose It!, 939t Low-density lipoprotein (LDL)-cholesterol atorvastatin (Lipitor), 117-118, 124, 131, 139, 146, 153, 158, 893, 1015 HMG-CoA reductase inhibitors, 123, 129, 238, 527, 636, 675, 893 ILLUMINATE trial, 146 rosuvastatin, 124, 893 simvastatin, 117-118, 123, 131, 139, 153, 158-159, 766, 893 Low-density lipoproteins (LDLs), 386 LST design. See Large, simple trials Lubiprostone, 381, 382, 383, 383f Lung cancer studies, 215f, 221f, 222, 223, 372, 403

M Ma Huang. See Ephedra Macro level, ethical judgments, 569, 571, 581, 584, 589, 591, 592, 593, 596, 598, 599, 601 Major compendia, 63t, 64t, 82 Maleficence, nonmaleficence principle, 574-575, 580, 585, 586, 586f, 589 Malpractice protection, 512, 520-523, 522t Managed care organizations (MCOs), 19, 274, 283, 609 Managed care pharmacy AMCP drug evaluation monographs, 671, 683, 691 formulary submissions in AMCP format, 975 P&T committees, 609 therapeutic interchange, 636 medication information specialists and, 20-21 Management Standards, TJC, 633, 681, 722-723, 882 Mann-Whitney test, 138, 251, 399, 414, 415, 416 Mantel-Cox test. See Log-rank tests Mantel-Haenszel chi square test, 242, 396t, 403 MapMyRun, 939t Marchione v. State, 511 Marginal cost-utility ratios, 287 Marketing. See Advertising MARs. See Medication administration records Martindale: The Complete Drug Reference, 64t, 73

1296

INDEX

Massachusetts Coalition for the Prevention of Medical Errors, 819t Massachusetts Medical Society, Journal Watch, 87, 167 Masthead, 480, 481 Matched pairs design, 378, 417 Matched t test. See Paired-samples t test Matching, in case-control studies, 222 Material issue of fact, 530 Mauchly’s sphericity test, 419-420, 423 Maximum-tolerated-dose (MTD), 203 Mayo Clinic newsletter, 513 McMaster University, EBM and, 314-315 McNemar test of change, 395t, 425-426, 427 MCOs. See Managed care organizations MEADERS. See Medication Error and Adverse Drug Event Reporting System Mean, 135t, 136-138, 136f, 137f, 357. See also Central tendency Mean serum total cholesterol, 404-405 Meaningful Use program, 1045, 1046, 1051, 1063, 1064 Measurement errors, 229-230 Measures of association, 106, 154, 156-159, 157t Measuring quality of care. See Quality improvement Mechanism classification, ADRs, 752 MedCounselor Sheets, 911, 912 Median, 135t, 136-138, 136f, 137f, 357 Median test, 395t, 415 Medical affairs, HPs and, 977 Medical devices, ad hoc P&T committee for, 622 Medical education CME ACCME, 504, 551-552 described, 336, 504, 551, 977 HPs and, 977 Medical errors, 778, 782, 820, 1046, 1062. See also Medication errors Medical Errors (organization), 819t Medical ethics. See Ethics Medical informatics, 20. See also Pharmacy informatics Medical information, HPs and, 975, 975f Medical information requests fulfillment, 982-984 Medical Literature Retrieval and Analysis System (MedLARS), 5 Medical Matrix, 532 Medical records. See Electronic medical records Medical science liaisons (MSLs), 976, 976t Medical Subject Heading (MeSH) terms, 83, 85, 166, 324, 341, 532 Medicare Improvements for Patients and Providers Act of 2008 (MIPPA), 529-530 Medicare Part B, off-label use and, 529-530 Medication administration records (MARs), 789

Medication and patient safety. See also ADRs; Adverse drug events; Adverse drug reactions; Medication errors; Medication misadventures Institute for Safe Medication Practices, 690, 744t, 793, 795, 811, 814, 819t, 922 introduction, 778-779 organizations promoting best practices, 819t pharmacovigilance, 743, 750, 759, 764-766, 776, 962 P&T committees, 621 safety as priority, 820-821 safety event analysis, 795-797 Medication Error and Adverse Drug Event Reporting System (MEADERS), 756 Medication errors. See also Adverse drug events; Adverse drug reactions; Medication and patient safety; Medication misadventures ADEs-ADRs-medication errors-medication misadventures relation, 742, 742f, 779-782, 781f case studies, 797, 802, 806, 819-820 classification of, 787-790 defined, 779-782 education, 810-811 To Err Is Human: Building a Safer Health System, 778, 782, 810, 820, 1049 to err is human, 778, 798-800 identification of, 784-787 impact on patients and health care systems, 782-783 Just Culture, 802-806 key points of, 780-781 management principles, 816-819 Medical Errors (organization), 819t medical errors and, 778, 782, 820, 1046, 1062 MERP, 621, 792, 793 NCCMERP, 779, 780, 780t, 781, 787, 791, 796, 821, 824 patient outcomes classification, 791-792 prevention best practices, 811-816 P&T committees and, 646 questions in defining, 782t reporting of, 784-787, 792-793 risk factors, 806-809 safety event analysis, 795-797 self-assessment questions, 821-824 suggested readings, 827-828 Trigger Tool, 785 Medication Errors Reporting Program (MERP), 621, 792, 793. See also National Coordinating Council for Medication Error Reporting and Prevention Medication factors, in making responses and recommendations, 42t-43t Medication guides, 848, 917, 991, 991t, 1008

INDEX

Medication information. See also Drug information drug information compared to, 3 for patient population, 1, 2 patient-specific, 1, 2 self-assessment questions, 25-28 services, 4t Medication information provision. See Drug information provision Medication information specialists. See Drug information specialists; Pharmacists Medication misadventures, 741-828. See also Adverse drug events; Adverse drug reactions; Medication and patient safety; Medication errors ADEs-ADRs-medication errors-medication misadventures relation, 742, 742f, 779-782, 781f adverse drug reactions, 741-776 Medication Therapy Management services (MTM services), 933, 947 Medication usage patterns, 905 Medication use evaluation (MUE). See also Quality improvement acronyms for, 719-720, 720t control limits, 704, 714, 727-728, 727f, 730 corrective actions for, 721-722 criteria and, 717-718, 723-725, 725t data analysis for, 730-731 data collection for, 728-730 follow-up evaluation, 732 goal of, 718 interventions for, 731-732 medication use process, 718-721, 719t in performance improvement programs, 704, 717-718 performance indicators and, 725t, 726-728 process for, 721-732 responsibility for MUE function, 721-722 topic selection, 722-723 Medication use patient care policies, 874t. See also Policies Medication use process. See also Pharmacy informatics; Quality improvement defined, 1047 MUE and, 718-721, 719t pharmacy informatics and, 1047-1048 Medication use system quality improvement. See Quality improvement Medicinal drug promotions. See Drug promotions MedLARS. See Medical Literature Retrieval and Analysis System MEDLINE, 5, 63t, 64t, 86, 88, 108t, 237, 254, 324, 341, 349, 508, 532, 543, 910 MedlinePlus, 63t, 91, 94t, 943, 944, 954t MEDMARX, 756, 787, 793 Medscape, 11, 64t, 67, 76, 167, 915

1297

MedWatch program ADRs, 750, 756, 757, 759-761, 762, 764, 769 counterfeit drugs, 647 REMS, 690 Meeting abstracts, 112, 474t, 514 Meetings, P&T committees, 623-624 Merck Delmuth Development Corp. v. Merck & Co., 515 Merck Index, 63t, 69, 515 Merck Manuals computer malfunctions and, 532 consumer-base version, online, 94t Merck Manual of Diagnosis and Therapy, 64t, 76 Merck Manual of Geriatrics, 71 Merck Veterinary Manual, 64t, 80 online viewing, 11 MERP. See Medication Errors Reporting Program MeSH. See Medical Subject Heading Meso level, ethical judgments, 569, 571, 572, 581, 584, 587, 589, 590, 591, 592, 593, 594, 596, 598, 599, 601 Meta-analyses case study, 244-245 design of, 190t, 237t, 238-245, 240f, 243f hetereogeneity in, 239, 241, 242, 243f, 245 homogeneity in, 234, 241, 242, 244, 245 Metformin, 54, 55, 56, 200, 508 Methacholine challenge, 407-408 Methadone, 509, 918t Methemoglobinemia, benzocaine-induced, 9 Methods for the Economic Evaluation of Health Care Programmes, 302 Methods section. See Controlled clinical trials Me-too drugs, 615, 674, 1021 Metronidazole, 582, 584, 585 Meyler’s Side Effects of Drugs, 63t, 67, 750 MI. See Myocardial infarction Micro level, ethical judgments, 569, 571, 572, 582, 584, 590, 591, 592, 593, 598, 599, 601 Micromedex 2.0, 64t, 65, 68, 72, 73, 94, 508, 875, 1028 Micromedex Healthcare Series’ Detailed Drug Information for the Consumer, 913 Microsoft Excel, decision analysis with, 295 Microsoft HealthVault, 935t, 1061 Middle technical style, 464t, 465 Minimal evidence, dietary supplements, 254-255 Minipress. See Prazosin Minor, ADR classification, 751 Minoxidil, 51, 52, 53 MIPPA. See Medicare Improvements for Patients and Providers Act of 2008 Misadventures. See Medication misadventures Misinformation, drug promotions, 1027-1030, 1029t Mixed between-within ANOVA, 381, 383, 420-423

1298

INDEX

MLA Style Manual, 465 Mobile health (smartphones, mobile devices) AHFS Drug Information, 65 drug information references, 82 patient sources of drug information, 938, 939t-940t, 940-942, 941t smartphones health information on, 10, 60 QR codes, 484, 486 Mode, 135t, 136-138, 136f, 137f, 357 Moderate, ADR classification, 751 Modern Pharmacology With Clinical Applications, 74 Monitoring, pharmacy informatics, 1058 Monitoring errors, 787, 790. See also Adverse drug reactions Moral development stages, 593 Morbidity and Mortality Weekly Report, 228 Morgan v. Wal-Mart Stores, 510 Morphine, 124, 133 Mothers. See Women MSLs. See Medical science liaisons MSNBC, 92t MTD. See Maximum-tolerated-dose MTM services. See Medication Therapy Management services MUE. See Medication use evaluation Multicollinearity, 436, 441, 450 Multidisciplinary guideline development groups, 317t, 320. See also Evidence-based clinical practice guidelines Multinomial distribution, 368t Multinomial logistic regression, 437 Multiple linear regression, 396t, 399, 412, 428, 433-436, 437, 440, 446 Multiple sclerosis, 202, 370, 371 Multivariate linear regression. See Multiple linear regression Mushrooms, poisonous, 515 MyFitnessPal, 939t Myocardial infarction (MI), 13, 123, 220, 239, 718, 812t, 814, 1022 MyPHR, 1061 MyPlate, 939t MyQuitCoach, 940t

N N-of-1 trials. See also Controlled clinical trials; Study designs case studies compared to, 202t, 226 described, 200-202 evaluation, 201-202 purpose, 190t NACDS. See National Association of Chain Drug Stores Nagelkerke R2, 440 Naloxone, 754t, 794t

NaRCAD. See National Resource Center for Academic Detailing Narrative review, 190t, 234-235, 236, 237t Narrow therapeutic index (NTI), 210, 637 National Association of Chain Drug Stores (NACDS), 780t, 931 National Cancer Institute, 86, 857 National Center for Complementary and Alternative Medicine, 94t National Center for Health Statistics, 405 National Center for Immunization and Respiratory Diseases, 920 National clinical trials registry, 11, 63t, 165, 237, 836 National Committee for Quality Assurance (NCQA), 627, 633 National Community Pharmacists Association (NCPA), 931 National Comprehensive Cancer Network’s Drugs and Biologics Compendium, 529, 855 National Coordinating Council for Medication Error Reporting and Prevention (NCCMERP), 779, 780, 780t, 781, 787, 791, 796, 821, 824. See also Medication errors National Council on Patient Information and Education (NCPIE), 17, 780t National Guideline Clearinghouse (NGC), 312, 341, 876, 908 National Health and Nutrition Examination Survey (NHANES), 405 National Institutes of Health (NIH) CHI provided by, 94t clinical trial types, 377 ClinicalTrials.gov, 11, 63t, 165, 237, 836 consensus statements, 342 FDA-NIH joint venture, 685 as funding source, 163 HSTAT, 342, 909 INDs, 832 Mobile, 954t pharmacogenomics and, 685 Protection of Human Subjects, 837 PubMed Central, 90 purpose, 987t National Institutes of Health Office of Biotechnology Activities (NIH/OBA), 836 National Library of Medicine (NLM) CHI, 94t HSTAT Web site, 342, 909 human practice guidelines, articles, 107 indexing service, 83 Loansome Doc, 90 MedLARS, 5 MEDLINE, 5, 63t, 64t, 86, 88, 108t, 237, 254, 324, 341, 349, 508, 532, 543, 910 MedlinePlus, 63t, 91, 94t, 943, 944, 954t RxNorm, 1062 as secondary resource, 108t video, on evaluating Web sites, 91, 944

INDEX

National Medical Library, 541 National Patient Safety Goals (NPSGs), 620, 622, 722-723, 790, 811-812, 816, 882, 888f National Poison Data System (NPDS), 22 National Quality Forum (NQF), 620, 783t, 813, 921 National Resource Center for Academic Detailing (NaRCAD), 1033-1034, 1035 National Weather Service, liability, 516 National Women’s Health Information Center, CHI and, 94t Natural Medicine Comprehensive Database, 63t, 69-70 Natural Medicines Watch, 762 Natural products. See also Dietary supplements case study, 256-257 Review of Natural Products, 63t, 70 Natural Standard, 63t, 70 NBC, 92t NCCMERP. See National Coordinating Council for Medication Error Reporting and Prevention NCPA. See National Community Pharmacists Association NCPIE. See National Council on Patient Information and Education NCQA. See National Committee for Quality Assurance NDAs. See New Drug Applications Negative binomial distribution, 367-368, 368t Negative formularies, 634-635 Negative studies, 109-110, 164. See also Controlled clinical trials Negligence. See also Liability defenses to employer defenses, 520 individual defenses, 518-519 malpractice protection, 512, 520-523, 522t failure to warn actions, 504, 508, 509, 523, 525, 527 for inappropriate analysis or dissemination of information, 517-518 for inappropriate quality information, 513-517 for incomplete information, 509-511 Negligent hire theory, 520 Negligent misrepresentation, 513, 527 Neofax, 64t Nephrotoxicity, 57, 512 Nested t test. See Paired-samples t test Nesting, 339, 398, 400, 458 Network meta-analysis, 238-239 Neumega®. See Oprelvekin Neuropathy, 169-170, 201, 259, 527 Neutrophils, ASTIN trial, 204-205, 208 New Drug Applications (NDAs) defined, 833 increase, 23 NI trials and, 192 submission of, 844 New England Journal of Medicine, 112, 464, 483

1299

New Molecular Entities (NMEs), 831, 833, 838, 845, 848 New products, formularies, 638-639 New York Times v. Tasini, 543 New Zealand, DTCA legislation, 1017 News sources, online, 91, 92t Newsletters. See also Professional writing copying, 543 Web sites and, 477-485 Nexium®. See Esomeprazole Neyman bias, 206f NGC. See National Guideline Clearinghouse NHANES. See National Health and Nutrition Examination Survey NHST. See Null hypothesis significance testing NI trials. See Noninferiority trials NIH. See National Institutes of Health NIH/OBA. See National Institutes of Health Office of Biotechnology Activities NLM. See National Library of Medicine NMEs. See New Molecular Entities NNT. See Number-needed-to-treat No difference, equivalency and, 160-161 No-blinding, 125-126, 125t Nominal data defined, 134, 135t measures of association, 106, 154, 156-159, 157t Nominal scale, 355 Nominal variables. See Discrete variables Nonadherence, attrition and, 142, 143, 144, 145, 378, 379, 380 Noncurrent cohort study. See Retrospective cohort study Nonexperimental designs, 374, 376, 423. See also Study designs Noninferiority trials (NI trials), 191-200 case studies, 198, 200 design concept, 193-194, 193f design example, 194, 194f evaluation, 196-197 increased use of, 192 NI margin, 192-193 purpose, 190t results interpretation, 193-196 Nonmaleficence, 574-575, 580, 585, 586, 586f, 589 Non-normal distributions, transforming, 368-370, 369f Nonparametric tests. See also Statistical tests for between-group differences testing, Mann-Whitney test, 138, 251, 399, 414, 415, 416 for differences from population testing binomial test, 395t, 406 Kolmogorov-Smirnov one-sample test, 395t, 407 for nominal and categorical data

1300

INDEX

Nonparametric tests (Cont.) Fisher’s exact test, 396t, 401, 402-403, 415, 425 kappa statistic, 396t, 403-404 Mantel-Haenszel chi square test, 242, 396t, 403 Pearson’s chi square test, 396t, 401-402 parametric tests versus, 392-393 for relationship or association testing logistic regression, 381, 384, 391, 396t, 437-441, 442, 446, 448, 454 Spearman rank-order correlation coefficient, 396t, 428, 436-437 survival analysis, 441-450 for within-group differences testing Cochran Q test, 395t, 426-427 Friedman two-way ANOVA by ranks, 395t, 424 McNemar test of change, 395t, 425-426, 427 sign test, 395t, 425 Wilcoxon signed-rank test, 395t, 423, 424, 425 Nonpreferred drug products, 634, 676 Nonprescription drugs Dietary Supplement and Nonprescription Drug Consumer Protection Act of 2006, 762 Handbook of Nonprescription Drugs: An Interactive Approach to Self-Care, 63t, 66 PDR for Nonprescription Drugs, 67 Nonpublic unsolicited requests, 979 Nonpunitive attribute, CQI, 706t Nonresponse bias, 206f, 230 Nonresponse error, 230 Non-small cell lung cancer, 287 Nonsteroidal anti-inflammatory drugs (NSAIDs), 116, 169. See also CONDOR trial Nonsystematic review. See Narrative review Normal distribution described, 359f, 364-365, 365f standard, 365-366, 365f transforming non-normal distributions, 368-370, 369f NOT, Boolean operator, 83-84, 84f Notice of Privacy Practices, 549 NPDS. See National Poison Data System NPSGs. See National Patient Safety Goals NQF. See National Quality Forum NSAIDs. See Nonsteroidal anti-inflammatory drugs NTI. See Narrow therapeutic index Null hypothesis (H0), 115, 139, 195, 195f, 198, 200, 386-388, 387t Null hypothesis significance testing (NHST), 386, 387t Number-needed-to-treat (NNT), 106, 156-159, 157t, 215, 1024t

Nuremberg Code, 837 Nurses’ Health Study, 220 Nutrition, ad hoc P&T committee for, 622

O Obama administration, consumer “privacy bill of rights,” 550 OBRA. See Omnibus Budget Reconciliation Act Observational study designs, 213-226. See also Controlled clinical trials; Study designs biases in, 205, 206t-207t, 208 case-control studies, 190t, 214t, 221-224, 221t, 224t characteristics of, 214t cohort studies, 190t, 214-220, 214t, 215f, 397 cross-sectional studies, 190t, 214t, 224-226, 397 interventional trials compared to, 188, 213 overview, 213 Observer bias, 201 Odds ratios case-control studies, 224, 224t epidemiological statistics, 371-373 meta-analysis, forest plot, 243f Office for Generic Drugs, FDA, 210 Office of Inspector General (OIG), 551, 987t Office of the Secretary, 986, 986t, 1063 Off-label use DTCA and, 1019t, 1026, 1027 informed consent and, 527-530, 528t OIG. See Office of Inspector General Omeprazole, 143, 162, 198, 262, 615, 1021. See also CONDOR trial; Gastroesophageal reflux disease Omission negligence, 513, 520-521 Omnibus Budget Reconciliation Act (OBRA), 509, 691, 932 Omnibus test, 397-398, 401, 409, 416, 419, 422, 424, 427, 432, 435, 445, 448 Oncoplatin, 297-300, 299t Oncotaxel, 297-300, 299t Oncovin. See Vincristine One Medicine, 78, 81 One-sample t test, 395t, 405-406 one-sample z test, 395t, 404-405 One-tailed hypothesis tests, 138, 139, 386, 388, 400 One-way between-groups ANOVA (one-way ANOVA), 384, 394, 395t, 408-410, 411, 412, 415, 416, 432 One-way repeated measures ANOVA, 418-420, 421, 422, 423, 424 Online forums, 936t Open cohort, 216 Open formularies, 633-635 Oprelvekin (Neumega® ), 47-50 OR, Boolean operator, 83-84, 84f Oral contraceptives, 127, 512, 992

INDEX

Order entry, 1049-1050. See also Computerized provider order entry (CPOE) Ordinal data, 134, 135t Ordinal scale, 355 Organ impairment, dosage recommendations and, 63t Orphan Drug Act, 848-849 Orphan drugs, 617t, 675t, 829, 830, 848-849. See also Investigational drugs Osteoarthritis. See also CONDOR trial botanicals and, 253 chondroitin, 95 Outcome measurement, cohort studies, 219 Outcomes Donabedian framework and, 709 ECHO model, 709, 736 in pharmacoeconomic equation, 275f Outcomes assessment. See Pharmacoeconomics Outcomes research. See also Pharmacoeconomics defined, 275 health outcomes research defined, 246 HR-QOL, 188, 247-252, 285, 709 QOL measures, 190t, 246-252, 249f HPs and, 976 pharmacoeconomics relationship with, 275 Outline, of professional writing projects, 466, 467 Overpromotion, of drugs, 526-527 Oxandrolone, 122

P p-values, 106, 138, 140, 145, 150-152, 151f, 153, 154, 155, 160-161, 387-388 Pace, H. A., 263, 272 Package inserts. See Patient package inserts Paired-samples t test, 383, 395t, 417-418, 419, 423, 425 Pair-wise meta-analysis, 238 Palladone Extended-Release. See Hydromorphone hydrochloride extended-release capsules Parallel forms, 230 Parallel-groups design, 117, 377-378, 380-382. See also Controlled clinical trials Parameter negligence, 513 Parametric tests. See also Statistical tests for between-group differences testing ANCOVA, 138, 381, 384, 395t, 398, 412-414, 435 factorial between-groups ANOVA, 395t, 410-412, 420, 421, 422 independent-samples t test, 135, 138, 381, 384, 391, 395t, 400, 407-408, 409, 414 for differences from population testing one-sample t test, 395t, 405-406 one-sample z test, 395t, 404-405

1301

nonparametric tests versus, 392-393 for relationship or association testing multiple linear regression, 396t, 399, 412, 428, 433-436, 437, 440, 446 Pearson’s product-moment correlation, 396t, 428-430, 429f, 431, 432, 434, 436, 437, 441, 450 simple linear regression, 394, 396f, 397, 430-433, 434, 435 for within-group differences testing mixed between-within ANOVA, 381, 383, 420-423 one-way repeated measures ANOVA, 418-420, 421, 422, 423, 424 paired-samples t test, 383, 395t, 417-418, 419, 423, 425 Parathyroid hormone, 379, 434, 435 Parkas v. Saary, 509 Parkinson disease, 145, 381 Parnate. See Tranylcypromine sulfate Patent expiration, 632, 633, 1015 Patent information, 79, 675 Patient education, 932-934. See also Community pharmacy practice Patient factors, in making responses and recommendations, 42t Patient Information section, of drug evaluation monograph, 691 Patient Monitoring Guidelines, of drug evaluation monograph, 691 Patient outcomes, classifying, 791-792. See also Medication errors Patient package inserts (PPIs), 509, 848, 982f, 991-992, 991t Patient pocket formularies, 629 Patient population, medication information for, 1, 2 Patient preference assessments, HR-QOL measurements, 247 Patient preference measures (utility scores), 285-286, 287, 297, 298, 298t, 299t, 300, 301 Patient safety. See also Medication and patient safety; Medication misadventures committees, 722, 818 information, in drug evaluation monograph, 690-691 Patient Safety and Clinical Pharmacy Services (PSPC) Collaborative, 921-922 Patient Safety and Quality Assurance page, APhA, 922 Patient Safety Organizations (PSOs), 777, 786-787, 790, 793, 821 Patient searches, ambulatory care drug information, 902-904 Patient-centered care, 5, 14, 778, 810, 818, 921, 931, 964, 1046, 1060 Patient-centered medical homes (PCMHs), 5, 8, 901, 903

1302

INDEX

Patient-professional relationship, 580, 584, 586f, 1019 Patients background information, responses to drug information queries, 35, 37, 39, 39t, 40, 43t, 44, 46, 47, 48, 51, 54 counseling, risk assessment and management, 511-513 drug information in ambulatory care disposal of unused medications, 916-919, 918t education, 915-916 foreign, 121 inclusion/exclusion in clinical trials, 118-121 medication errors’ impact on, 782-783 Patients, Interventions, Comparison, Outcome (PICO), 323, 327 PatientsLikeMe.com, 935t Patient-specific medication information, 1, 2 Paxil, 527 PBMs. See Pharmacy benefit managers PCMHs. See Patient-centered medical homes PDAs. See Personal digital assistants PDCA. See Plan-Do-Check-Act PDR. See Physician’s Desk Reference PDSA. See Plan-Do-Study-Act PDUFA. See Prescription Drug User Fee Act of 1992 Pearson’s chi square test, 396t, 401-402 Pearson’s product-moment correlation, 396t, 428-430, 429f, 431, 432, 434, 436, 437, 441, 450 Pediatric vaccine formularies, 631 Pediatrics American Academy of Pediatrics, 75 dosage recommendations, 64t, 73 drug use, summary of evidence table, 686-687, 686t, 687t-688t Pediatric Dosage Handbook, 73 Pediatrics Today, 89 Peer-review controlled clinical trials, 111-113 unpublished studies, 238, 532 Pelargonium sidoides, bronchitis and, 256-257 Perez v. Wyeth Laboratories Inc., 525, 526 Performance improvement MUE and, 704, 717-718 P&T committees and, 608, 624, 722 Performance indicators. See also Quality improvement MUE process, 725t, 726-728 quality improvement, 709-717 Performance/Quality Improvement committee, 722 Permission, copyrighted material, 466t, 468, 471, 472, 473, 489. See also Professional writing Per-protocol (PP) analysis, 144-145, 380 Personal digital assistants (PDAs), 77, 82 Personal health records (PHRs), 1060, 1061

Perspectives, in pharmacoeconomic analysis, 273, 277, 279, 282, 283, 288, 296, 300 Pet Education, 64t Pet Place, 64t, 80-81 Pets with Diabetes, 64t, 81 Pfizer ASTIN study, 204-205, 208 Clear Health Communication Initiative, 946, 954 PGY1. See Postgraduate year one PGY2. See Postgraduate year two Pharmaceutical industry. See also Food and Drug Administration ADEs and, 984-985 case studies, 980-981, 984, 985, 992, 993, 999-1000 FDA communication of drug safety information, 988 drug information resources, 995, 996t-997t, 998 opportunities within, 1000-1002 organization of offices and centers, 989f regulation by, 978-980 safety communication tools, 988, 990 federal agencies and, 986-993 health care professionals opportunities, 974, 974f customer response center, 974-975 marketing, 977 medical affairs, 977 medical education, 977 medical information, 975, 975f medical science liaisons, 976, 976t outcomes research, 976 publications, 978 medical division organization, 974f medical information requests fulfillment, 982-984 organizations in, 985-986, 986t regulation of, 978-981 REMS and, 990-991 support for educational activities, 550-554, 552t, 553t Pharmaceutical Research and Manufacturers of America (PhRMA) Code on Interactions With Health Care Professionals, 552t, 559, 972, 981, 1063 DTCA guidelines, 1018, 1019t guidance on pharmaceutical industry support for education, 504, 551-552, 553t pharmacist.com, 66, 69, 909, 920, 922. See also American Pharmacists Association Pharmacists. See also Community pharmacy practice; Drug information queries; Drug information specialists; Health care professionals defenses to negligence employer defenses, 520

INDEX

individual defenses, 518-519 malpractice protection, 512, 520-523, 522t evolution of role, 7-19 investigational drug role of, 853-859 liability of for inappropriate analysis or dissemination of information, 517-518 for inappropriate quality information, 513-517 for incomplete information, 509-511 for negligence and failure to warn, 504, 508, 509, 523, 525, 527 in promoting rational pharmacotherapy, 35, 36 role of, evolution, 7-19 support of P&T committees, 615-620, 616t The Pharmacist’s Guide to Evidence-Based Medicine for Clinical Decision Making (Bryant & Pace), 263, 272 Pharmacodiagnostics, 692 Pharmacoeconomic analysis, 287-295 challenges with, 287 cost-consequence analysis, 283 decision analysis alternatives specified, 293 calculations, 294, 294t, 295t decision analysis structure, 293-294 decision identification, 292-293 defined, 274, 292 enoxaparin-wayfarin comparison, 295 medical uses, 292 outcomes/probabilities specified, 293, 293t sensitivity analysis (vary cost estimates), 294-295 software, 295 steps, 290t, 292 discount rates, 277-278, 282, 291, 295 perspectives, 273, 277, 279, 282, 283, 288, 296, 300 sensitivity analysis for decision analysis, 294-295 in pharmacoeconomic analysis, 288t, 291 steps decision trees, 288t, 290-291, 290t, 291f, 292, 293, 294, 295 inputs and outcomes measurements, 288t, 289-290 pharmacoeconomics model selection, 288t, 289 probabilities for outcomes of treatment alternatives, 288t, 290 problem defining, 288, 288t resources identification (needed to conduct analysis), 288t, 290 results presentation, 288t, 292 sensitivity analysis, 288t, 291 study’s perspective, 288, 288t summary of steps, 288t treatment alternatives and outcomes, 288-289, 288t

1303

UTI example, 288-290, 291f, 292-295, 293t, 295t Pharmacoeconomics, 273-309 analysis models described, 275-276, 276t selecting, for pharmacoeconomic analysis, 288t, 289 conclusion, 302 cost assessment direct medical costs, 273, 276, 277, 279, 283, 289, 300 direct nonmedical costs, 273, 276, 277, 279 indirect costs, 273, 276, 277 intangible costs, 273, 276, 277, 279 societal costs, 277 timing adjustments for costs, 277-278 defined, 273, 274-275 educational opportunities, 302 Essentials of Pharmacoeconomics, 302 introduction, 274 Methods for the Economic Evaluation of Health Care Programmes, 302 odds ratios case-control studies, 224, 224t meta-analysis, forest plot, 243f outcomes assessment cost-benefit analysis, 274, 275, 276t, 279282, 281t, 285, 287, 289, 296 cost-effectiveness analysis, 274, 275, 276t, 282-284, 282t, 283t, 284t, 287, 289, 293, 296 cost-minimization analysis, 274, 275, 276t, 278-279, 279t, 287, 289, 296 cost-utility analysis, 273, 274, 276t, 285-287, 286t, 289, 292, 296, 300-301 pharmacoeconomic equation, 275f P&T committees and, 274 QALYs, 276t, 282, 285-286, 286t, 287, 289, 295, 298, 299t, 300, 301 ratios benefit-to-cost, 280, 281t, 282 CER, 282-284, 283t, 284t, 287, 294, 295t, 300 ICER, 283, 284, 284t, 295t, 300 incremental (marginal) cost-utility, 287 reviewing published literature BSC versus Oncoplatin and Oncotaxel (case study), 297-301, 298t, 299t steps, 296-297 self-assessment questions, 303-306 suggested readings, 309 Web sites references, 302, 302t Pharmacoeconomics & Outcomes News Weekly, 88 Pharmacoepidemiologic studies, 233. See also Postmarketing surveillance Pharmacogenomics defined, 685 drug development and, 843-844 emergence, 61 newsletter or Web site topic, 484t

1304

INDEX

Pharmacogenomics (Cont.) profiling, 15 theranostics and, 692 in Therapeutic Indications section of drug evaluation monographs, 685-686 Pharmacogenomics Knowledgebase, 685 Pharmacognosy, Trease and Evans’, 63t, 70 Pharmacokinetics Bioavailability/Pharmacokinetics section, drug evaluation monograph, 689 resources on, 64t, 73-74 Pharmacologic Data section, of drug evaluation monograph, 683 Pharmacology Clinical Pharmacology clinicalpharmacology.com, 63t, 64t, 65, 72, 94, 529, 530, 911, 912-913 Small Animal Clinical Pharmacology and Therapeutics, 80 Clinical Pharmacology OnHand, 82 Pharmacopoeia, British, 73. See also U.S. Pharmacopoeia Pharmacotherapy: A Pathophysiological Approach, 64t, 77, 876 Pharmacotherapy, rational, 35, 36 Pharmacotherapy Casebook: A Patient-Focused Approach, 77 Pharmacotherapy Handbook, 77 Pharmacotherapy Principles and Practice, 64t, 77, 876 Pharmacovigilance, 743, 750, 759, 764-766, 776, 962 Pharmacy and Therapeutics (P&T) committees, 607-668. See also Drug evaluation monographs; Formularies ad hoc committees, 620-622 case studies, 624-625, 639, 649-650 communication within organization cost, budget, and forecasting, 650-651 investigational review board actions, 650 liaison with other elements of organization, 651-652 synchronization of databases, 652 conclusion, 652 credentialing and privileges, 644 defined, 608-609 discussion questions, 652-653 drug evaluation by, pharmacoeconomic methods, 274 evidence-based clinical guidelines, 642 formulary committees compared to, 608-609 functions, 624 introduction, 608-609 meetings, 623-624 organizational background, 609-615 chairperson for, 613 historical, 609-610 hospital organization, 611, 611f operating policy of, 613 voting members of, 612

PBMs, 609, 613-615 performance improvement and, 608, 624, 722 pharmacist support, 615-620, 616t policies and procedures, 639-642 quality improvement functions ADRs, 645 counterfeit drug products, 647 illegible handwriting, transcription, and abbreviations, 646 medication error incidents, 646 medication quality assurance, 645 product shortages, 648-650 safety alert, 647-649 timeliness of order fulfillment, 647 recalls, 648 role of, 609 self-assessment questions, 653-655 standard order set development, 642-644 Pharmacy benefit management organizations, 613-615 Pharmacy benefit managers (PBMs) drug evaluation monographs, 672 medication information specialists as, 19 outcomes research specialists and, 976 P&T committees, 609, 613-615 Pharmacy informatics, 1045-1072 acute care pharmacy setting, 1056-1057 administration, 1058 case studies, 1060, 1061, 1064 CDSSs, 1047, 1048, 1050, 1053-1054, 1055 community pharmacy setting, 1057-1058 conclusion, 1066 defined, 1049, 1066 dispensing, 1055 future, 1060-1061 health care system, 1060-1061 health information technologies, 1047-1048 introduction, 1046-1047 medication use process, 1047-1048 monitoring, 1058-1060 MUE and, 718-721, 719t self-assessment questions, 1066-1069 suggested readings, 1071-1072 transcription, 1054-1055 Pharmacy information management systems (PIMs), 1057 Pharmacy literature. See Primary literature Pharmacy practice. See also Community pharmacy practice APPEs, 18, 24, 955, 958-959, 964 IPPEs, 24, 958 Pharmacy Practice and the Law, 64t, 75 A Practical Guide to Contemporary Pharmacy Practice, 63t, 69 Verified Internet Pharmacy Practice Sites, 532 Pharmacy Quality Alliance (PQA), 705, 712 Pharmacy students. See Drug information education and training Pharmacy-Based Immunization Delivery, 920

INDEX

Pharmacy/biomedical literature. See Drug information resources Phases of clinical trials. See also Investigational drugs; Postmarketing surveillance Phase I trials, 830, 837, 843, 848, 849 Phase II trials, 843, 846, 848, 849 Phase III trials, 839, 843, 844, 846, 848 Phase IV trials, 233, 847-848, 977, 978 Phenytoin, 518-519 PHI. See Protected health information Phosphodiesterase 5 inhibitors, 88 PhRMA. See Pharmaceutical Research and Manufacturers of America PHRs. See Personal health records Physician’s Desk Reference (PDR), 64t, 66-67 Compendium of Veterinary Products compared to, 79 DI search tools, 525 Libertelli v. Hoffman La Roche, Inc. & Medical Economics Co., 515 PDR for Herbal Medicines, 63t, 66, 70 PDR for Nutritional Supplements, 67 PDR for Ophthalmic Medicines, 67 PDRBooks, 66 PICO. See Patients, Interventions, Comparison, Outcome PIMs. See Pharmacy information management systems Pink Sheet, 92t Placebo. See also Controlled clinical trials active control compared to, 121, 139, 191-192, 197, 832 blinding and, 125-126 when not to use, 123 Placebo creep (biocreep), 197 Placebo effects, N-of-1 trials, 201 Plagiarism, 471-473, 545-546, 851. See also Copyright Plan-Do-Check-Act (PDCA), 704, 704f, 707-708, 720, 735. See also Quality improvement Plan-Do-Study-Act (PDSA), 817 Platform presentations, 486, 487-491 Platykurtic distribution, 362 Playboy Enterprises, Inc. v. Universal Tel-A-Talk, Inc., 536 Plumb’s Veterinary Drug Handbook, 9, 64t, 78-79 Point estimate, 155-156, 327 POISINDEX, 64t Poison control AAPCC, 22 Animal Poison Control Center, 9, 80 Casarett & Doull’s Toxicology: The Basic Science of Poisons, 64t, 77 medication information service, 4t National Poison Data System, 22 poison information centers, lawsuits, 517-518 poisonings, liability, 517-518 specialty practices, 19, 21-22 Toxic Exposure Surveillance System, 22

1305

Toxicities/Known Adverse Effects section, drug evaluation monographs, 690 toxicology Casarett & Doull’s Toxicology: The Basic Science of Poisons, 64t, 77 Goldfrank’s Toxicologic Emergencies, 64t, 77 Poisonous mushrooms, 515 Poisson distribution, 367-368, 368t Policies (health system policies) defined, 872, 873 definitions section in, 879-880 documentation section, 881 drug policy development, medication information provision, 13-17 medication use patient care, 874t procedure section, 881 P&T committees development of, 639-642 purpose statement, 879 references section, 881-882 sample classifications, 873t Policy development (health system policy development), 871-882. See also Projects conclusion, 893 contact other institutions, 877-879 drug information resources, 875-876 introduction, 872 standardized and systematized approach, 875 Policy statement, 880-881 Polyneuropathy, 527 Poorly designed trials, 109, 237, 252. See also Controlled clinical trials Popular technical style, 464t, 465 Populations, samples and, 353-354 Positive formularies, 634-635 Post hoc NI margin, 197 Post hoc significance bias, 207t Post hoc tests, 381-382, 409, 411, 416, 419, 421, 422, 424, 427, 435, 439, 445 Poster presentations, 486, 491-492 Postgraduate year one (PGY1), 956, 960, 962, 963 Postgraduate year two (PGY2), 956, 961-962, 970 Postmarketing surveillance. See also ADRs adverse drug events, 9-10, 234 adverse drug reactions, 758-759 defined, 758 dietary supplement ADR reporting, 761-762 DTCA and, 1022 pharmaceutical industry and, 973 pharmacoepidemiologic studies, 233 poison control centers, 22 purpose, 190t study design type, 190t, 233-234 vaccine ADR reporting, 764 Posttest, pretest-posttest design, 417, 418, 421, 423, 424, 425 Potassium supplement, 754t, 794t

1306

INDEX

Power (statistical power) controlled clinical trials, 132-134, 139-142 DTCA evaluation, 1024t errors and, 386-387 McNemar test, 426 meta-analyses, 239 paired-samples t-test, 417 parametric tests, 393 purpose of, 387 repeated measures ANOVA, 418 sample size and, 231 statistical significance and, 150-152, 151f study design and, 375 Power algorithm, 258, 258f Power analysis, 132-134, 139-142 PP analysis. See Per-protocol analysis PPIs. See Patient package inserts PQA. See Pharmacy Quality Alliance PR Newswire, 92t Practice guidelines. See Evidence-based clinical practice guidelines Pradaxa. See Dabigatran Prazosin (Minipress), 511 Preclinical testing, 837 Predictive values, epidemiological statistics, 373-374 Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), 241, 470 Pregnancy drugs, safety, 519 Drugs in Pregnancy and Lactation, 64t, 75 Prescribing errors, 782t, 787, 790 in medication use process, 719t Prescription Access Litigation Project, 526-527 Prescription Drug User Fee Act of 1992 (PDUFA), 835 Presentations, professional writing, 485-493. See also Professional writing Pretest-posttest design, 417, 418, 421, 423, 424, 425 Prevalence, epidemiological statistics, 371 Prevalence surveys. See Cross-sectional studies Prevalence-incidence bias, 206f prima facie, 513 Primary author, 463, 877 Primary endpoint, 129-131 Primary literature. See also Controlled clinical trials; Drug information resources advantages, 89 disadvantages, 89 evaluation of published studies abstract, 110t, 114-115 acknowledgments, 110t, 163 bibliography, 110t, 162-163 discussion/conclusion section, 110t, 149-150 introduction section, 110t, 115-116

journals, peer-review, and investigators, 111-113 methods section, 110t, 116-142 results, 110t, 142-148 results interpretation, 110t, 150-162 title, 114 examples, 108t obtaining, 89-90 types, 89 Princeton University Press v. Michigan Document Services, Inc., 541 Principles, for ethical analysis, 578-582 Principles of Biomedical Ethics, 572, 605 PRISMA. See Preferred Reporting Items for Systematic Reviews and Meta-Analysis Privacy. See also Legal aspects, of drug information practice communication, 549-550 ethics inquiry and, 581, 586f HIPAA, 535, 546-549, 550, 1065 Privileges and credentialing , P&T committees, 644 Probabilistic methods, ADR causality, 746, 749-750 Probabilistic sensitivity analysis, 291 Probability of adverse drug reactions, 745-751 sensitivity and specificity, epidemiological statistics, 373-374 Probability distributions, 363-370 binomial distribution, 366-367, 368, 368t, 377, 406 list, 367-368, 368t normal distribution, 359f, 364-365, 365f Poisson distribution, 367-368, 368t standard normal distribution, 365-366, 365f transforming non-normal distributions, 368-370, 369f Probability values. See p-values Procedure section, in policies, 881 Process, Donabedian framework and, 708 Product claim ads, 1012, 1015-1018, 1024t Product information resources, 63t, 65-67 Product labeling, 838 Product shortages, P&T committees and, 648-650 Professional writing, 459-502 advantages, 460-461 article proposal, 461, 461f audience, 464, 466t, 467 of newsletters and Web sites, 478 of presentations, 487 of professional writing projects, 464, 466t, 467 case studies, 477, 485, 493 coauthor selection, 462-463 defined, 460 disadvantages, 461

INDEX

document sections, 469-475 bibliography, 474-475 body, 470-473 conclusion, 473-474 introduction, 470 editing, 469 as necessary skill, 493 newsletters and Web sites, 477-485 plagiarism, 471-473 platform presentations, 486, 487-491 poster presentations, 486, 491-492 presentations, 485-493 purpose of, 460 referees in, 476-477 self-assessment questions, 493-496 steps galley proofs, 476 preparing to write, 461-466, 462f, 464t revision of document, 469, 475-476 rules of writing, 466-469, 466t submission of document, 475 technical writing styles middle technical style, 464t, 465 popular technical style, 464t, 465 pure technical style, 464-465, 464t topic selection newsletters and Web sites, 483, 484t for professional writing, 461-462 Web posting, 492-493 Web sites and newsletters, 477-485 Professional-patient relationship, 580, 584, 586f, 1019 Profiling, pharmacogenomic, 15 Programmatic research, 190t, 212-213 Project Destiny, 931, 947 Projects (health system projects). See also Policies; Policy development defined, 882 design, 882-890 project closeout, 891 project definition, 883 project description, 883-886 project development, 886-890 project implementation, 890 project management, 891-892 project management software, 886-889, 890, 892 Promising, Professional Obligations, and the Refusal to Provide Service article, 596 Proportions, epidemiological statistics, 370-371 Propoxyphene (Darvon), 233, 648 Prospective attribute, CQI, 706t Prospective cohort study, 216 Prospective studies, 111, 376, 377. See also Adaptive clinical trials; Controlled clinical trials; Randomized controlled trials Protamine, 754t, 794t

1307

Protected health information (PHI), 546, 1046, 1063, 1064-1066 Protection of Human Subjects, 837 Protocol based clinical trials, 377. See also Adaptive clinical trials; Controlled clinical trials; Randomized controlled trials Protocol-defined pulmonary exacerbations, 171 Protopathic bias, 223 Provision, of drug and medication information. See Drug information provision Proximate cause, 506, 507, 509, 510 Pseudoephedrine, 410-411 Pseudo-R2, 440, 449 PSOs. See Patient Safety Organizations PSPC Collaborative. See Patient Safety and Clinical Pharmacy Services Collaborative P&T committees. See Pharmacy and Therapeutics committees Public unsolicited requests, 979-980 Publication bias, 110, 164, 165, 237-238, 239, 240, 240f, 241, 245, 329-330 Publication Manual of the American Psychiatric Association, 465 Publications, HPs and, 978. See also Professional writing Publisher requirements, professional writing, 465 PubMed, 11, 88, 108t, 114, 166, 237, 254, 508, 532, 876, 909, 910. See also MEDLINE PubMed Central, 90 Pulmonary disease, COPD, 122, 914 Pulmonary exacerbations, 170-171 Pure Food and Drugs Act, 834 Pure technical style, 464-465, 464t Purity and quality, dietary supplements, 255 Purpose statement, in policies, 879

Q Q test, Cochran, 395t, 426-427 QA. See Quality assurance QALYs. See Quality adjusted life years QOL measures. See Quality of life measures QR codes. See Quick Response codes Quackwatch, 11 Qualitative systematic review. See Systematic review Quality dietary supplements, 255 of evidence, 257 Quality adjusted life years (QALYs), 276t, 282, 285-286, 286t, 287, 289, 295, 298, 299t, 300, 301. See also Pharmacoeconomics Quality assurance (QA) drug information in ambulatory care, 921-923 as liability reducing factor, 521, 522t P&T committee for, 622

1308

INDEX

Quality improvement (health care quality in medication use system), 703-740. See also Medication use evaluation; Pharmacy and Therapeutics committees acronyms for, 706 attributes, 706-707, 706t case studies, 717, 724, 726 conclusion, 734 CQI, 312, 340, 706t, 709, 719-720 defined, 706 in drug information practice, 732-734 ECHO model, 709, 736 FOCUS-PDCA model, 704, 704f, 707-708, 720, 735 introduction, 704 measuring quality Donabedian framework, 704, 708-709, 736 purpose, 705-706 PDCA model, 704, 704f, 707-708, 720, 735 performance indicators MUE process, 725t, 726-728 quality improvement, 709-717 self-assessment questions, 735-737 Six Sigma quality, 720 Quality measures, CMS, 921 Quality of care. See Quality improvement Quality of information, Web 2.0, 531-533 Quality of life (QOL) measures, 190t, 246-252, 249f Quality of life studies, Therapeutics section of drug evaluation monographs, 689 Quantitative systematic review. See Meta-analyses Quantitative variables. See Continuous variables Quasi-experimental designs, 374, 375-376. See also Controlled clinical trials; Study designs Questions. See Drug information queries Quick Response codes (QR codes), 484, 486 Quoting, in professional writing projects, 471, 472

R Radiocontrast-induced nephropathy (RCN), 133-134 Random sampling, 229, 298, 301, 353-354 Randomization in clinical trials, 127-129 stratification, 128-129, 171, 219 Randomized controlled trials (RCTs). See also Adaptive clinical trials; Controlled clinical trials; Study designs adaptive clinical trials, 203-208 ASTIN study, 204-205, 208 benefits, 204, 208 biases, 205, 206t-207t, 208 purpose, 190t RCTs compared to, 377, 378-379

ADRs and, 765 cost, 375 crossover designs, 378, 382-383 equivalency trials, 191 experimental designs as, 374-375 interventional trials, 188, 213 ITT approach, 144-145, 168, 197, 256, 380 limitations, 375 N-of-1 trials case studies compared to, 202t, 226 described, 200-202 evaluation, 201-202 purpose, 190t NI trials, 191-200 case studies, 198, 200 design concept, 193-194, 193f design example, 194, 194f evaluation, 196-197 NI margin, 192-193 purpose, 190t results interpretation, 193-196 other study design types, 187, 188-189 outcomes research compared to, 14 parallel-groups design, 117, 377-378, 380-382 per-protocol approach, 144-145, 380 protocol based, 377 quasi-experimental designs compared to, 374, 375-376 superiority trials, 191 Range, 135t, 136-138 Ranitidine, thrombocytopenia and, 40, 41t, 54-57, 1059 Rates, epidemiological statistics, 370-371 Rating scales (RS), 285 Ratio data, 134, 135t Ratio scale, 356 Rational pharmacotherapy, promotion of, 35, 36 Ratios benefit-to-cost, 280, 281t, 282 CER, 282-284, 283t, 284t, 287, 294, 295t, 300 epidemiological statistics, 370-371 hazard, 159-160 ICER, 283, 284, 284t, 295t, 300 incremental (marginal) cost-utility, 287 odds ratios case-control studies, 224, 224t meta-analysis, forest plot, 243f RCA. See Root cause analysis RCN. See Radiocontrast-induced nephropathy RCTs. See Randomized controlled trials re Michael A. Gabert, 513 re Pharmatrak Inc. v. Privacy Litigation, 550 Reactions Weekly, 63t, 64t, 88, 751 Reasonable care, 507-508, 513 Reben v. Ely, 517 Recall bias, 207f, 223 Recalls, P&T committees and, 648 Rechallenge, in ADRs, 41t, 746, 747-748, 750 Recombinant interleukin-11. See Oprelvekin

INDEX

Recommendation/request, SBAR technique, 816 Recommendations. See also Drug evaluation monographs; Evidence-based clinical practice guidelines; Responsesrecommendations, to drug information queries evidence-based clinical practice guidelines, IOM standards articulate recommendations, 318t, 331-333 evidence foundations and rating strength of recommendations, 318t, 326-331 for study designs, 188, 257-259, 258f Recruitment, for clinical trials, 120-121 Red Book, Drug Topics, 63t, 68 Reeves v. Pharmajet, Inc., 512 Referees, in professional writing, 476-477 Reference bias, 237 Reference Manager, 474 References section, for policies, 881-882. See also Bibliography; Drug information resources Registry, ClinicalTrials.gov, 11, 63t, 165, 237, 836 Regression, homogeneity of, 413 Regression analysis. See also Survival analysis logistic regression, 381, 384, 391, 396t, 437-441, 442, 446, 448, 454 multiple linear regression, 396t, 399, 412, 428, 433-436, 437, 440, 446 simple linear regression, 394, 396f, 397, 430-433, 434, 435 Regulation of pharmaceutical industry. See Pharmaceutical industry Regulatory agencies, 970-981. See also Code of Federal Regulations; Food and Drug Administration; Pharmaceutical industry; The Joint Commission Regulatory project manager, 833 Relationship tests nonparametric tests logistic regression, 381, 384, 391, 396t, 437-441, 442, 446, 448, 454 Spearman rank-order correlation coefficient, 396t, 428, 436-437 survival analysis, 441-450 parametric tests multiple linear regression, 396t, 399, 412, 428, 433-436, 437, 440, 446 Pearson’s product-moment correlation, 396t, 428-430, 429f, 431, 432, 434, 436, 437, 441, 450 simple linear regression, 394, 396f, 397, 430-433, 434, 435 Relative risk (RR) cohort studies, 214-215 controlled clinical trials, 155-159, 157t in epidemiological statistics, 371-372 Relative risk reduction (RRR), 155-159, 157t

1309

Reminder advertisements, 1012, 1015-1018, 1025t Remington: The Science and Practice of Pharmacy, 63t, 69 REMS. See Risk Evaluation and Mitigation Strategies Repeated measures ANOVA. See One-way repeated measures ANOVA Repeated measures design, 379, 417 Repertorio Farmaceutico Italiano, 73 Reporting. See Adverse drug events; Adverse drug reactions; Medication errors Reporting bias. See Publication bias Reports without control group. See Study designs Request/recommendation, SBAR technique, 816 Research, for professional writing projects, 466t, 467 Research designs. See Study designs Research hypothesis (H1), 115, 116, 134, 139, 152, 195, 195f, 198, 200, 386, 388 Research studies. See Study designs Resources. See Drug information resources; Ethics Respect for autonomy. See Autonomy principle Respect for persons, 575, 581 Respondeat superior doctrine, 520, 521 Response letters, for medical information request, 982-983 Response rates, survey research, 232 Responses-recommendations, to drug information queries, 35-58 accepting responsibility and eliminating barriers, 37 analysis and synthesis of information, 44 assessing critical factors, 40-41, 42t-43t, 43 background information and patient data, 35, 37, 39, 39t, 40, 43t, 44, 46, 47, 48, 51, 54 building database, 40-41, 42t-43t, 43 case studies, 46-57 conclusion, 45 desired characteristics of, 44-45, 45t follow-up, 45 identifying genuine need, 37-40 introduction, 36 questions to consider in, 39-40, 39t, 41t self-assessment questions, 57 Responsibilities ambulatory care clinicians, 904-916 medication use evaluation, 721-722 Restatement (Second) of Torts, §315, Negligent Misrepresentation Involving Risk of Physical Harm, 513 Restrictions in drug evaluation monographs, 678-680 Epocrates and, 907

1310

INDEX

Restrictive interventions, with MUE, 731 Results interpretation, NI studies, 193-196 Results section, controlled clinical trials. See Controlled clinical trials Retrospective cohort study, 216 Retrospective data collection, for MUE, 729 Retrospective studies. See Case-control studies Reuters Health News, 92t Review articles meta-analyses, 190t, 237t, 238-245, 240f, 243f narrative review, 190t, 234-235, 236, 237t overview, 234-235 systematic review, 190t, 234-235, 236-238, 237t Review of Natural Products, 63t, 70 Reviewers. See Referees Reviewing, professional writing, 469 Revision, professional writing, 469, 475-476 Rheumatoid arthritis. See also CONDOR trial drug promotion assessment, 1031 HR-QOL measurements, 249 ρ. See Spearman rank-order correlation coefficient Risk. See also Relative risk attributable, 215 interpreting, 156-159, 157t Risk assessment, patient counseling, 511-513 Risk Evaluation and Mitigation Strategies (REMS) liability and, 510 Patient Safety section of drug evaluation monographs, 390-391 pharmaceutical industry and, 990-991 Risk factors case-control studies on, 221-224 cohort studies on, 214-215 for medication errors and events, 806-809 Risk management, patient counseling, 511-513 Robustness, 230, 392 Rofecoxib (Vioxx), 154, 233, 529, 764, 1022 Roman v. City of New York, 515 Root cause analysis (RCA), 778, 796-797, 801 Rosiglitazone (Avandia), 330, 407-408, 409, 530, 764-765 Rosuvastatin, 124, 893 Rote Liste, 73 RR. See Relative risk RRR. See Relative risk reduction RS. See Rating scales rs. See Spearman rank-order correlation coefficient Rule-based errors, 777, 778, 798-800 Rule-based noncompliance, 799 Rules for ethical analysis, 578-582 professional writing, 466-469, 466t Rules engines, 1048

Run-in phase, 119-120 RunKeeper, 940t Rx, in pharmacoeconomic equation, 275f RxNorm, 1062

S SafeMedication.com, 94, 94t Safety. See also Medication and patient safety; Patient safety alert, P&T committees and, 647-649 controlled clinical trials, 145-146 as priority, 820-821 safety event analysis, 795-797 SAMHSA. See Substance Abuse and Mental Health Services Administration Sample frame, 229, 231-232 Sample means, 138, 155, 385, 389, 405 Sample size controlled clinical trials, 132-134 power analysis and, 132-134 statistical power and, 231 survey research, 231 Sampling cluster, 354 convenience, 354 populations and, 353-354 random, 229, 298, 301, 353-354 stratified random, 129, 354 survey research, 232 systematic, 354 Sampling distributions, 385-386. See also Central limit theorem Sampling error, 229 Sanderson v. Eckerd Corporation, 510 SBAR, 816, 821 Scales of measurement, 355-356 Scatterplots, 362, 363, 364f, 429f, 430, 433, 436 Scheffé test, 409, 411 Scientific Style and Format: The CBE Manual for Authors, Editors, and Publishers, 465 Scientific writing, 3, 19, 24, 25 SD. See Standard deviation Secondary journals, 105, 167 Secondary resources. See Drug information resources Security, of information communication privacy, 549-550 HIPAA, 535, 546-549, 550, 1065 Selection bias, 119-120, 206t, 217-218, 220, 238, 240, 325 Selective reporting, 109, 251 Self-assessment questions. See specific entries Self-care movement. See also Direct-toconsumer advertising Handbook of Nonprescription Drugs: An Interactive Approach to Self-Care, 63t, 66 pharmacist’s role, 15-17

INDEX

SEM. See Standard error of the mean Sensitivity, epidemiological statistics, 373-374 Sensitivity analysis for decision analysis, 294-295 in pharmacoeconomic analysis, 288t, 291 Serum 25-hydroxyvitamin D, 376, 434 Serum creatinine, 129, 434, 435, 724t, 755, 794t Serum parathyroid hormone, 434-435 Severe, ADR classification, 751 Severity classification, ADRs, 751-752 SG. See Standard gamble Shame, culture of, 777, 802, 804 Shape measures, descriptive statistics, 360, 361f, 362 Shewhart Cycle, 706. See also Plan-DoCheck-Act Shortages, drug, 648-650 Side Effects of Drugs Annual, 63t, 67, 750 Sign test, 395t, 425 Signed-rank test. See Wilcoxon signed-rank test Significance. See Clinical significance; Statistical significance Significance bias, 207t Sildenafil, 88, 426-427 Silver argyrol, 513 Silver nitrate, 513 Silverstein v. Penguin Putnam, 542 Simple and direct, professional writing, 466t, 467-468 Simple linear regression, 394, 396f, 397, 430-433, 434, 435 Simvastatin, 117-118, 123, 131, 139, 153, 158-159, 766, 893 Single-blinding, 125-126, 125t Situation, SBAR technique, 816 Six Sigma, 720 Skewness, 360, 361f, 362, 365, 367, 369, 377, 392, 415 Skill-based errors, 777, 778, 798-800, 803 Sleep deprivation, cohort study, 218-219 Slides, in presentations, 486, 487, 488, 489, 490, 493 Small Animal Clinical Pharmacology and Therapeutics, 80 Smartphones. See also Mobile health health information on, 10, 60 QR codes, 484, 486 SMARxT DISPOSAL campaign, 917 Snell and Cox R2, 440 Social media. See also Community pharmacy practice for CHI, 10-11, 934, 935t-936t, 937-939 DDI and, 998-999 liability concerns, 535 Social networks, 929, 930, 934, 936t, 937 Societal costs, pharmacoeconomics, 277 Sodium chloride infusion, 133, 515, 878

1311

Sodium polystyrene sulfonate, 754t, 794t Software. See also Computer-based clinical decision support systems; Lexicomp; Mobile health ACT study design, 204 bibliographic, 474-475 Computer Software Act of 1980, 543 decision analysis, pharmacoeconomic analysis, 295 drug interaction, Baker v. Arbor Drugs, Inc, 509 Epocrates, 67, 82, 532, 907 e-prescribing, 1052 liability for, 516 for presentations, 487, 488, 489, 490, 492, 493 project management software, 886-889, 890, 892 Turnitin, 545-546 SOJA. See System of Objectified Judgment Analysis SOPs. See Standard operating procedures Spearman rank-order correlation coefficient (rs, ρ), 396t, 428, 436-437 Special Protocol Assessments, 204 Specialist, drug information and, 4 Specialty practices. See Drug information specialists Specialty training. See Drug information education and training Specificity, 373-374 Spelling and grammar, professional writing, 466t, 467 Sphericity, 419-420, 423 Split-plot ANOVA. See Mixed between-within ANOVA Sponsor, 833 Sponsor-investigator, 833 Spontaneous Reporting System (SRS), 759, 765 SRS. See Spontaneous Reporting System Stability, resources on, 63t, 72 Stability studies, 190t, 209-210 Staged protocol, 203. See also Adaptive clinical trials Standard deviation (SD) controlled clinical trials, 135t, 136-138, 136f, 137f in normal distribution curve, 137f variance and, 358, 360 Standard error of the mean (SEM), 138, 155, 229, 385-386 Standard gamble (SG), 285-286 Standard operating procedures (SOPs), 853, 972, 978, 980 Standard order set development, P&T committees, 642-644 Standard therapy. See Active therapy Standardization, of dietary supplements, 253

1312

INDEX

Statins atorvastatin (Lipitor), 117-118, 124, 131, 139, 146, 153, 158, 893, 1015 cardiac events, 376 DTCA spending, 1014 HMG-CoA reductase inhibitors, 123, 129, 238, 527, 636, 675, 893 ILLUMINATE trial, 146 one-tailed hypothesis, 386 rosuvastatin, 124, 893 simvastatin, 117-118, 123, 131, 139, 153, 158159, 766, 893 usage, Veterans Administration patients, 438 Statistical analysis, 351-458. See also Descriptive statistics; Epidemiological statistics; Inferential statistics; Probability distributions; Statistical tests conclusion, 450-451 controlled clinical trials, 134-139, 135f, 136f, 137f data measurement and variables, 355-356 experimental designs, 374-375 flawed studies and, 450-451 introduction, 352-353 nonexperimental designs, 374, 376, 423 populations, 353-354 purpose, 134 quasi-experimental designs, 374, 375-376 sampling, 353-354 self-assessment questions, 451-454 study designs and, 451 suggested readings, 457-458 variables and data measurement, 355-356 Statistical power. See Power Statistical significance alpha and p values, 387-388 clinical difference versus, 150-152, 151f clinical significance versus, 352, 387-392 confidence intervals, 389-391, 389f, 390f Statistical tests. See also Statistical analysis ANCOVA, 138, 381, 384, 395t, 398, 412-414, 435 ANOVA controlled clinical trials, 138 factorial between-groups, 395t, 410-412, 420, 421, 422 Friedman two-way ANOVA by ranks, 395t, 424 Kruskal-Wallis one-way ANOVA by ranks, 395t, 415-416 mixed between-within, 381, 383, 420-423 one-way between-groups, 384, 394, 395t, 408-410, 411, 412, 415, 416, 432 one-way repeated measures, 418-420, 421, 422, 423, 424 binomial test, 395t, 406 Cochran Q test, 395t, 426-427 degrees of freedom, 393, 401, 402, 405, 408, 409, 421 evaluating, 451

Fisher’s exact test, 396t, 401, 402-403, 415, 425 independent-samples t test (Student’s t-test), 135, 138, 381, 384, 391, 395t, 400, 407-408, 409, 414 kappa statistic (κ), 396t, 403-404 Kolmogorov-Smirnov one-sample test, 395t, 407 logistic regression, 381, 384, 391, 396t, 437-441, 442, 446, 448, 454 log-rank tests, 445-446 Mann-Whitney test, 138, 251, 399, 414, 415, 416 Mantel-Haenszel chi square test, 242, 396t, 403 McNemar test of change, 395t, 425-426, 427 median test, 395t, 415 multiple linear regression, 396t, 399, 412, 428, 433-436, 437, 440, 446 omnibus test, 397-398, 401, 409, 416, 419, 422, 424, 427, 432, 435, 445, 448 one-sample t test, 395t, 405-406 one-sample z test, 395t, 404-405 overview, 353, 400-401 paired-samples t test, 383, 395t, 417-418, 419, 423, 425 Pearson’s chi square test, 396t, 401-402 Pearson’s product-moment correlation, 396t, 428-430, 429f, 431, 432, 434, 436, 437, 441, 450 post hoc tests, 381-382, 409, 411, 416, 419, 421, 422, 424, 427, 435, 439, 445 selection process, 394-400, 395t, 396t sign test, 395t, 425 simple linear regression, 394, 396f, 397, 430-433, 434, 435 Spearman rank-order correlation coefficient (rs, ρ), 396t, 428, 436-437 survival analysis, 441-450 Cox regression, 220, 441, 445-450, 448f Kaplan-Meier method, 441, 442, 443-445, 443f, 444f, 446, 447, 448, 449 life tables, 215, 441, 442, 443 overview, 441-442 Wilcoxon signed-rank test, 395t, 423, 424, 425 Stedman’s medical dictionary, 82 Stepwise approach, finding drug information resources, 60 Stockley’s Drug Interactions, 63t Stratification, 128-129, 171, 219 Stratified random sampling, 129, 354 Streaming presentations, 493 Strict liability, 511, 514-515, 516 Structure, Donabedian framework and, 708 Structured abstracts, 114, 167, 341 Student Understanding of the Relationship between the Health Professions and the Pharmaceutical Industry report, 593-594 Students. See Drug information education and training

INDEX

Student’s t test. See Independent-samples t test Study Commission on Pharmacy, 7 Study designs (beyond basic controlled trials), 187-272. See also Controlled clinical trials; Ten Major Considerations Checklist adaptive clinical trials (ACTs), 203-208 ASTIN study, 204-205, 208 benefits, 204, 208 biases, 205, 206t-207t, 208 purpose, 190t RCTs compared to, 377, 378-379 bioequivalence trials, 190t, 210-212 case reports, 190t, 226-227 case series, 190t, 226-227 case studies (study design type) described, 226-227 N-of-1 trials compared to, 202t, 226 purpose, 190t clinical decisions/recommendations, 188, 257-259, 258f clinical practice guideline types, 245-246 conclusion, 259 controlled clinical trials, 117-118 dietary supplement medical literature, 252-257 duration of studies, 254 evidence lack, 254-255 international trials and information retrieval, 253-254 quality and purity, 255 size of trials, 254 special considerations, 255-256 standardization of supplements, 253 testing natural products (case study), 256-257 flawed studies, statistical analysis, 450-451 fundamental elements, 111 health outcomes research defined, 246 QOL measures, 190t, 246-252, 249f introduction, 188-191 list of types, 190t N-of-1 trials case studies compared to, 202t, 226 described, 200-202 purpose, 190t NI trials, 191-200 case studies, 198, 200 design concept, 193-194, 193f design example, 194, 194f evaluation, 196-197 NI margin, 192-193 purpose, 190t results interpretation, 193-196 nonexperimental designs, 374, 376, 423 observational study designs, 213-226 biases in, 205, 206t-207t, 208 case-control studies, 190t, 214t, 221-224, 221t, 224t

1313

characteristics of, 214t cohort studies, 190t, 214-220, 214t, 215f, 397 cross-sectional studies, 190t, 214t, 224-226, 397 interventional trials compared to, 188, 213 overview, 213 overview, 190-191 postmarketing surveillance adverse drug events, 9-10, 234 adverse drug reactions, 758-759 defined, 758 dietary supplement ADR reporting, 761-762 DTCA and, 1022 pharmaceutical industry and, 973 pharmacoepidemiologic studies, 233 poison control centers, 22 purpose, 190t study design type, 190t, 233-234 vaccine ADR reporting, 764 programmatic research, 190t, 212-213 quasi-experimental, 374, 375-376 randomization and, 127-129 reports without control group, 226-227 case reports, 190t, 226-227 case series, 190t, 226-227 case studies (study design type), 190t, 202t, 226-227 review articles meta-analyses, 190t, 237t, 238-245, 240f, 243f narrative review, 190t, 234-235, 236, 237t overview, 234-235 systematic review, 190t, 234-235, 236-238, 237t self-assessment questions, 259-262 stability studies, 190t, 209-210 statistical analysis and, 451 statistical power and, 375 suggested readings, 272 survey research, 188, 190t, 227-233 types, 190t, 374-376 unpublished studies, 209-210, 237-238, 532, 538, 539 in vitro studies, 46, 47, 209-210, 252, 254, 255, 837, 840, 843 when to use, 259 Style sheets, of newsletters and Web sites, 480 Subgroup analysis, 147-148 Subject consent by, 124-125 demographics of, 142-143 dropouts and adherence by, 143-145 investigational drugs and, 833 Submission, professional writing project, 475 Substance Abuse and Mental Health Services Administration (SAMHSA), 987t Suggested readings. See specific entries Sulfanilamide, 834 Sulfonylurea, 148, 200

1314

INDEX

Summary of evidence table, in Therapeutic Indications section, 686-687, 686t, 687t, 688t Summary page, of drug evaluation monograph. See Drug evaluation monographs Superiority trials, 191 Supplements. See Dietary supplements; Herbal supplements Surrogate endpoints, 131, 146, 329, 685, 847 Survey research, 188, 190t, 227-233 Survival analysis, 441-450 Cox regression, 220, 441, 445-450, 448f Kaplan-Meier method, 441, 442, 443-445, 443f, 444f, 446, 447, 448, 449 life tables, 215, 441, 442, 443 overview, 441-442 Synchronization of databases, P&T committees, 652 Synthesis analysis and synthesis in case studies, 46, 50, 53, 56 defined, 44 NI margin and, 196 of responses to drug information queries, 44 System error, medication errors from, 800-802 System of Objectified Judgment Analysis (SOJA), 631 Systematic review design of, 190t, 234-235, 236-238, 237t evidence-based clinical practice guidelines development standard, 317t, 320-325 Systematic sampling, 354 Systems oriented, CQI, 706t Systems/management control, in medication use process, 719t

T t test independent-samples, 135, 138, 381, 384, 391, 395t, 400, 407-408, 409, 414 one-sample, 395t, 405-406 paired-samples, 383, 395t, 417-418, 419, 423, 425 TASS. See Ticlopidine Aspirin Stroke Study Tavist-D. See Clemastine fumarate/ phenylpropanolamine hydrochloride TCA. See Tricyclic antidepressant TEACH Act. See Technology, Education and Copyright Harmonization Act Team-oriented attribute, CQI, 706t Technical writing styles. See also Professional writing middle technical style, 464t, 465 popular technical style, 464t, 465 pure technical style, 464-465, 464t Technology, ADRs and, 755-758. See also Health information technology; Informatics; Information technology

Technology, Education and Copyright Harmonization Act (TEACH) Act, 472, 544, 544t, 568 Telemedicine, liability concerns for, 533-535 Telephone Consumer Protection Act, 549-550 Templates, of newsletters and Web sites, 480 Ten Major Considerations Checklist, 189. See also Study designs ACTs, 208 bioequivalence studies, 212 case series, case reports, case studies, 227 case-control studies, 224 cohort studies, 220 cross-sectional studies, 226 DS studies, 255 HR-QOL measurements, 248 meta-analyses, 244 narrative reviews, 236 NI trials, 196, 200 N-of-1 studies, 202 postmarketing surveillance studies, 234 programmatic research studies, 213 stability studies, 210 survey research studies, 233 systematic reviews, 238 Tendon rupture, fluoroquinolone antibiotics, 527 Ten-step analysis, 273. See also Pharmacoeconomic analysis Ten-step process. See Medication use evaluation Teratogenicity, 64t, 75, 213, 227, 518, 519 Terminal nodes, in decision tree, 293-294 Tertiary resources. See Drug information resources TESS. See Toxic Exposure Surveillance System Testicular cancer, 468 Textbook of Therapeutics, 64t, 77 Textbook of Veterinary Internal Medicine, 9, 64t, 79 Thalidomide, 835 The Joint Commission (TJC) accreditation and certification manual, 696 ADR guidelines of, 753 ADR reduction by, 744t DI specialists practice standards, 505 drug evaluation monograph guidelines, 671 Management Standards, 633, 681, 722-723, 882 medication use process, 718-721, 719t National Patient Safety Goals, 620, 622, 722-723, 790, 811-812, 816, 882, 888f P&T committees and, 609 for removal of agents, 681-682 Theophylline, 508, 794t Theranostics, 692. See also Pharmacogenomics Therapeutic committees. See Pharmacy and Therapeutics committees Therapeutic Indications section. See Drug evaluation monographs

INDEX

Therapeutic interchange defined, 635 drug evaluation monographs and, 682 formularies PBMs, 614 P&T committees, 635-637 Therapeutic Potential classifications, FDA, 617, 617t Therapeutic Rating classification, FDA, 674, 675t Therapeutic Research Faculty Ident-a-Drug, 63t, 71-72 Natural Medicine Comprehensive Database, 63t, 69-70 Therapeutic substitution, 627, 635-636, 814, 1033 Therapy evaluation, resources on, 64t, 76-77 Thompson v. Western States Medical Center, 526 Thomson Reuters bibliographic software, 474 BIOSIS Previews, 64t, 85 Current Contents Connect, 63t, 86, 324 gettingwell.com, 94t International Pharmaceutical Abstracts, 63t, 64t, 87, 108t, 114, 237, 876 3+3 trial, 203. See also Adaptive clinical trials Thresholds, for MUE, 726-728, 730 Thrombocytopenia with chemotherapy, 48, 50 cyclophosphamide and, 48 heparin-induced, 56-57, 785 ranitidine and, 40, 41t, 54-57, 1059 Thrombosis Antithrombotic Therapy and Prevention of Thrombosis, 112, 319 venous, 129 Ticlopidine Aspirin Stroke Study (TASS), 148 Time trade-off (TTO), 247, 285, 286, 287, 297, 298, 300 Timeliness of order fulfillment, P&T committees and, 647 Timing adjustments, for costs, 277-278. See also Pharmacoeconomics Title, of clinical trial, 114 Title 21, CFR, 837, 978, 984, 1017. See also Code of Federal Regulations TN. See True negatives To err is human, 778, 798-800 To Err Is Human: Building a Safer Health System, 778, 782, 810, 820, 1049 Topic selection for MUE, 722-723 for newsletters and Web sites, 483, 484t for professional writing, 461-462 Topical steroids, 754t, 794t Torcetrapib, 146 Tort law, 505-518. See also Legal aspects, of drug information practice; Liability Total joint arthroplasty, 399-400 Total quality management (TQM), 706 Toxic Exposure Surveillance System (TESS), 22

1315

Toxicities/Known Adverse Effects section, drug evaluation monographs, 690 Toxicology. See also Poison control Casarett & Doull’s Toxicology: The Basic Science of Poisons, 64t, 77 Goldfrank’s Toxicologic Emergencies, 64t, 77 TP. See True positives TQM. See Total Quality Management Track changes feature, professional writing, 469 Trademark infringement, 536-537 Tramadol, 509 Tranexamic acid (TXA), 399-400 Transcription, pharmacy informatics, 1054-1055 Transcription errors, P&T committees and, 646 Transforming non-normal distributions, 368-370, 369f Transfusion service committee, P&T committee, 622 Transient effects, in cross-sectional studies, 225 Transparency evidence-based clinical practice guidelines, 317t, 319 GRADE system, 312, 331 health care, 703, 704-705, 734, 1063 P&T committees, 616 Tranylcypromine sulfate (Parnate), 509 Trease and Evans’ Pharmacognosy, 63t, 70 Treatment clinical trials, 377. See also Adaptive clinical trials; Controlled clinical trials; Randomized controlled trials Treatment order effects, 201 TreeAge Pro, 295 Tricyclic antidepressant (TCA)-amitriptyline study, 420-422 Trigger Tool, for medication errors and ADEs, 785 Triglyceride content, 436-437 TRIP. See Turning Research Into Practice Triple-blinding, 125t, 126 Trissel stability studies, 209 Trissel’s 2 Clinical Pharmaceutics Database, 63t, 65, 72 Trissel’s Handbook of Injectable Drugs, 63t, 72 Trissel’s Stability of Compounded Formulations, 63t, 69 Trohoc study. See Case-control studies True negatives (TN), 373-374 True positives (TP), 373-374 Truven Health Analytics CareNotes System, 913 Drug Topics Red Book, 63t, 68 DRUGDEX, 529, 530 Micromedex 2.0, 64t, 65, 68, 72, 73, 94, 508, 875, 1028 Micromedex Healthcare Series’ Detailed Drug Information for the Consumer, 913 TTO. See Time trade-off Tukey test, 409, 411

1316

INDEX

Turning Research Into Practice (TRIP), 909 Turnitin, 545-546 Twitter, 1061 2 x 2 contingency tables, 372, 372f, 401, 402, 403, 415 2 x 2 factorial design, 410, 411 Two-tailed hypothesis tests, 138, 139, 168, 386, 388, 400 TXA. See Tranexamic acid Type I errors, 139-142, 140t, 386-387 Type II errors, 139-142, 140t, 386-387

U Uniform Requirements for Manuscripts Submitted to Biomedical Journals, 113, 124, 465, 466, 475 Uniform resource locators (URLs), 482 Unique responses, for nonfrequently asked questions, 983-984. See also Pharmaceutical industry United States Adopted Name, 83 University Health System Consortium, 533 University of Iowa, IDIS service, 63t, 64t, 83, 87, 237, 877, 909 University of Kentucky Medical Center, 4 Unlabeled uses, 638 Unmasking bias, 206f Unpublished studies, 209-210, 237-238, 532, 538, 539 Unreliable drug information, Internet, 112-113 Unsolicited requests, 979-980 Unstructured abstracts, 114 Unused medication, disposal of, 916-919, 918t Updates, on evidence-based clinical practice guidelines, 318t, 334 Urinary tract infection (UTI) example, 288-290, 291f, 292-295, 293t, 295t. See also Pharmacoeconomic analysis URLs. See Uniform resource locators U.S. Pharmacist, 68 U.S. Pharmacopoeia (USP) adverse drug events, prevention, 744t, 783t APhA Patient Safety and Quality Assurance page, 922 Chapter 797 Pharmaceutical Compounding— Sterile Preparations, 878-879 Dictionary, 67 medication errors, 780-781 MEDMARX, 756, 787, 793 NCCMERP founding member, 780, 780t patient and medication safety, best practices, 819t Patient Safety Program, 690 resources published by, 63t, 67 stability studies, 210 veterinary medicine information, 63t U.S. Preventive Services Task Force (USPSTF), 342 USP. See U.S. Pharmacopoeia

USP Verified program, 70 USP-Dietary Supplement Verification Program (USP-DSVP), 255 USP/NF, 69 USPSTF. See U.S. Preventive Services Task Force Uterine cancer, 223 UTI example. See Urinary tract infection example Utility scores. See Patient preference measures

V VA Adverse Drug Event Reporting System, 757 Vaccination ADR reporting, 764 influenza, 371, 425-426, 512, 814 Vaccine Adverse Event Reporting System (VAERS), 757, 764, 770 Valdecoxib (Bextra), 233, 1027 Validity of criteria or indicators for MUE, 725, 725t external, 117t, 118, 120, 149, 151, 254, 255, 325, 725t internal, 116, 117t, 149, 151, 231, 255, 325, 1034 Valium. See Diazepam Value, health care and, 703 Value-driven health care, 704, 734 Vancomycin, 794t, 874 Variability measures, descriptive statistics, 358, 359f, 360 Variables. See also specific variables confounders, 122, 128, 207t, 217, 218-219, 355 scales of measurement, 355-356 Variance. See also Analysis of variance homogeneity of, 408, 410, 411, 412, 413, 417, 418, 420, 422, 423 standard deviation and, 358, 360 Venous thrombosis, 129 Veracity, 578, 581, 585, 586f, 589 Verified Internet Pharmacy Practice Sites, 532 Veterans Administration drug class reviews, 671 patients, statin use, 438 Veterinary Drug Handbook, Plumb’s, 9, 64t, 78-79 Veterinary medicine AMDUCA, 79, 81-82 Animal Poison Control Center, 9, 80 AVMA, 79-80, 81 Compendium of Veterinary Products, 64t, 79 compounding guidelines, 79-80 drug information resources, 64t, 78-81 FDA/CVM homepage, 79 Merck Veterinary Manual, 64t, 80 One Medicine, 78, 81 Pet Education, 64t Pet Place, 64t, 80-81 Pets with Diabetes, 64t, 81 Textbook of Veterinary Internal Medicine, 9, 64t, 79

INDEX

Veterinary Pharmacology and Therapeutics, 80 Vicarious liability, 512, 519, 556 Video-sharing, 936t Vincristine (Oncovin), 48 Vindesine and cisplatin (VP), 287 Vioxx®. See Rofecoxib Vitamin D parathyroid hormone, 379 serum 25-hydroxyvitamin D, 376, 434 vitamin D-calcium absorption study, 376-377 Vitamin K, 754t, 794t VP. See Vindesine and cisplatin

W Walter Reed Army Medical Center study, 280 Warfarin, 159-160, 295, 507, 644, 645, 683-684, 726, 887, 888f, 910, 921 Warner Bros. Ent’mt, Inc. v. RDR Books, 542 WBS. See Work breakdown structure Web 2.0. See also Social media Health 2.0, 1060-1061 liability concerns fraud and abuse, 535-537 quality of information, 531-533 social media, 535 telemedicine and cybermedicine, 533-535 patient sources of drug information, 929, 934 Web posting, 492-493. See also Professional writing Web sites. See specific Web sites WebMD® described, 936t FDA-WebMD joint online venture, 93 Medscape, 11, 64t, 67, 76, 167, 915 mobile app, 940t as tertiary resource, 108t Weibull distribution, 368t WHO. See World Health Organization Wikipedia, 531-532, 1060 Wikis, 531, 936t, 1060 Wilcoxon signed-rank test, 395t, 423, 424, 425 Williams & Wilkins Co. v. United States, 541 Willingness-to-pay (WTP) approach, 274, 280 Winter v. G.R. Putnam’s Sons, 515 Wisdom of crowds, 930, 937 Within-group differences testing nonparametric tests for Cochran Q test, 395t, 426-427 Friedman two-way ANOVA by ranks, 395t, 424 McNemar test of change, 395t, 425-426, 427 sign test, 395t, 425 Wilcoxon signed-rank test, 395t, 423, 424, 425

1317

parametric tests for mixed-between within ANOVA, 381, 383, 420-423 one-way repeated measures ANOVA, 418-420, 421, 422, 423, 424 paired-samples t test, 383, 395t, 417-418, 419, 423, 425 Wolters Kluwer Health Drug Facts and Comparisons, 65, 70, 71, 912 Drug Information Handbook, 66, 912 Geriatric Dosage Handbook, 63t, 71 International Pharmaceutical Abstracts, 63t, 64t, 87, 108t, 114, 237, 876 Pediatric Dosage Handbook, 73 Review of Natural Products, 63t, 70 Women AHA/ACC evidence-based guidelines for cardiovascular disease prevention in women, 322 breast cancer Kaplan-Meir method and, 443-445, 443f, 444f Oncoplatin vs.Oncotaxel, 297-300, 299t oprelvekin (Neumega®), 47-50 breast-feeding, levofloxacin, 75-76 cervical cancer, 215 endometrial cancer-coffee consumption study, 220 female sexual arousal disorder, 88-89 gender bias, 120 National Women’s Health Information Center, CHI and, 94t pregnancy drugs, safety, 519 Drugs in Pregnancy and Lactation, 64t, 75 uterine cancer, 223 Work breakdown structure (WBS), 887-889, 891 World Health Organization (WHO), 743, 744t, 1012, 1013-1014 World Wide Web. See Internet Writing. See Professional writing; Scientific writing WTP. See Willingness-to-pay approach WWW. See Internet X XML, 1061

Z z test, one-sample, 395t, 404-405 ZocDoc, 940t z-scores, 352, 366, 388, 392, 405, 452 ZygoControl Weight Loss, ADRs, 749-750, 764