[wiley series in probability and statistics] clinical trials (a methodologic perspective) ||...

CLINICAL TRIALS

WILEY SERIES IN PROBABILITY AND STATISTICS

Established by WALTER A. SHEWHART and SAMUEL S. WILKS

Editors: David J. Balding, Noel A. C. Cressie, Nicholas I. Fisher,Iain M. Johnstone, J. B. Kadane, Geert Molenberghs, Louise M. Ryan,David W. Scott, Adrian F. M. Smith, Jozef L. TeugelsEditors Emeriti: Vic Barnett, J. Stuart Hunter, David G. Kendall

A complete list of the titles in this series appears at the end of this volume.

CLINICAL TRIALS

A Methodologic Perspective

Second Edition

STEVEN PIANTADOSIJohns Hopkins School of Medicine, Baltimore, MD

A JOHN WILEY & SONS, INC., PUBLICATION

Copyright 2005 by John Wiley & Sons, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data:

Piantadosi, Steven.Clinical trials : a methodologic perspective / Steven Piantadosi—2nd ed.

p. ; cm. – (Wiley series in probability and statistics)Includes bibliographical references and index.ISBN-13: 978-0-471-72781-1 (cloth : alk. paper)ISBN-10: 0-471-72781-4 (cloth : alk. paper)1. Clinical trials—Statistical methods. I. Title. II. Series.

[DNLM: 1. Biomedical Research—methods. 2. Research—methods. 3. ClinicalTrials—methods. 4. Statistics—methods. W 20.5 P581c 2005]R853.C55P53 2005610′.72—dc22

2005040833

Printed in the United States of America.

10 9 8 7 6 5 4 3 2 1

CONTENTS

Preface xxi

Preface to the First Edition xxv

1 Preliminaries 1

1.1 Introduction, 11.2 Audience and Scope, 21.3 Other Sources of Knowledge, 4

1.3.1 Terminology, 51.3.2 Review of Notation and Terminology Is Helpful, 6

1.4 Examples, Data, and Programs, 61.5 Summary, 7

2 Clinical Trials as Research 9

2.1 Introduction, 92.1.1 Clinical Reasoning Is Based on the Case History, 102.1.2 Statistical Reasoning Emphasizes Inference Based on Designed

Data Production, 122.1.3 Clinical and Statistical Reasoning Converge in Research, 13

2.2 Defining Clinical Trials Formally, 142.2.1 Mixing of Clinical and Statistical Reasoning Is Recent, 142.2.2 Clinical Trials Are Rigorously Defined, 162.2.3 Experiments Can Be Misunderstood, 172.2.4 Clinical Trials as Science, 182.2.5 Trials and Statistical Methods Fit within a Spectrum of

Clinical Research, 19

v

vi CONTENTS

2.3 Practicalities of Usage, 202.3.1 Predicates for a Trial, 202.3.2 Trials Can Provide Confirmatory Evidence, 202.3.3 Clinical Trials Are Unwieldy, Messy, and Reliable, 212.3.4 Other Methods Are Valid for Making Some Clinical

Inferences, 232.3.5 Trials Are Difficult to Apply in Some Circumstances, 252.3.6 Randomized Studies Can Be Initiated Early, 25

2.4 Summary, 262.5 Questions for Discussion, 26

3 Why Clinical Trials Are Ethical 29

3.1 Introduction, 293.1.1 Science and Ethics Share Objectives, 303.1.2 Equipoise and Uncertainty, 31

3.2 Duality, 323.2.1 Clinical Trials Sharpen, but Do Not Create, the Issue, 323.2.2 A Gene Therapy Tragedy Illustrates Duality, 323.2.3 Research and Practice Are Convergent, 333.2.4 The Hippocratic Tradition Does Not Proscribe Clinical

Trials, 363.2.5 Physicians Always Have Multiple Roles, 38

3.3 Historically Derived Principles of Ethics, 413.3.1 Nuremberg Contributed an Awareness of the Worst Problems, 413.3.2 High-Profile Mistakes Were Made in the United States, 423.3.3 The Helsinki Declaration Was Widely Adopted, 423.3.4 Other International Guidelines Have Been Proposed, 443.3.5 Institutional Review Boards Provide Ethical Oversight, 453.3.6 Ethical Principles Relevant to Clinical Trials, 46

3.4 Contemporary Foundational Principles, 483.4.1 Collaborative Partnership, 483.4.2 Scientific Value, 493.4.3 Scientific Validity, 493.4.4 Fair Subject Selection, 493.4.5 Favorable Risk–Benefit, 503.4.6 Independent Review, 503.4.7 Informed Consent, 513.4.8 Respect for Subjects, 52

3.5 Methodologic Reflections, 533.5.1 Practice Based on Unproven Treatments Is Not Ethical, 533.5.2 Ethics Considerations Are Important Determinants of Design, 553.5.3 Specific Methods Have Justification, 57

CONTENTS vii

3.6 Professional Conduct, 593.6.1 Conflict of Interest, 593.6.2 Professional Statistical Ethics, 61


4 Contexts for Clinical Trials 65

4.1 Introduction, 654.1.1 Some Ways to Learn about Trials in a Given Context, 664.1.2 Issues of Context, 67

4.2 Drugs, 684.2.1 Are Drugs Special?, 704.2.2 Why Trials Are Used Extensively for Drugs, 71

4.3 Devices, 724.3.1 Use of Trials for Medical Devices, 734.3.2 Are Devices Different from Drugs?, 744.3.3 Case Study, 76

4.4 Prevention, 764.4.1 The Prevention versus Therapy Dichotomy Is Overworked, 774.4.2 Vaccines and Biologicals, 784.4.3 A Perspective on Risk–Benefit, 794.4.4 Methodology and Framework for Prevention Trials, 81

4.5 Complementary and Alternative Medicine, 824.5.1 The Essential Paradox of CAM and Clinical Trials, 844.5.2 Why Trials Have Not Been Used Extensively in CAM, 854.5.3 Some Principles for Rigorous Evaluation, 87

4.6 Surgery and Skill-Dependent Therapies, 884.6.1 Why Trials Have Not Been Used Extensively in Surgery, 904.6.2 Reasons Why Some Surgical Therapies Require Less Rig-

orous Study Designs, 924.6.3 Sources of Variation, 924.6.4 Difficulties of Inference, 934.6.5 Control of Observer Bias Is Possible, 944.6.6 Illustrations from an Emphysema Surgery Trial, 95

4.7 A Brief View of Some Other Contexts, 1014.7.1 Screening Trials, 1014.7.2 Diagnostic Trials, 1034.7.3 Radiotherapy, 103


5 Statistical Perspectives 107

5.1 Introduction, 107

viii CONTENTS

5.2 Differences in Statistical Perspectives, 1085.2.1 Models and Parameters, 1085.2.2 Philosophy of Inference Divides Statisticians, 1085.2.3 Resolution, 1095.2.4 Points of Agreement, 110

5.3 Frequentist, 1125.3.1 Binomial Case Study, 1135.3.2 Other Issues, 114

5.4 Bayesian, 1155.4.1 Choice of a Prior Distribution Is a Source of Contention, 1165.4.2 Binomial Case Study, 1175.4.3 Bayesian Inference Is Different, 119

5.5 Likelihood, 1205.5.1 Binomial Case Study, 1215.5.2 Likelihood-Based Design, 122

5.6 Afterthoughts, 1235.6.1 Statistical Procedures Are Not Standardized, 1235.6.2 Practical Controversies Related to Statistics Exist, 123


6 Clinical Trials as Experimental Designs 127

6.1 Introduction, 1276.1.1 Trials Are Relatively Simple Experimental Designs, 1286.1.2 Study Design Is Critical for Inference, 129

6.2 Goals of Experimental Design, 1296.2.1 Control of Random Error and Bias Is the Goal, 1296.2.2 Conceptual Simplicity Is Also a Goal, 1306.2.3 Encapsulation of Subjectivity, 1316.2.4 Leech Case Study, 131

6.3 Trial Terminology, 1326.3.1 Drug Development Traditionally Recognizes Four Trial

Designs, 1326.3.2 Descriptive Terminology Is Broader and Recognizes More

Types of Trials, 1336.4 Design Concepts, 134

6.4.1 The Foundations of Design Are Observation and Theory, 1346.4.2 A Lesson from the Women’s Health Initiative, 1366.4.3 Experiments Use Three Components of Design, 137

6.5 Survey of Developmental Trial Designs, 1436.5.1 Early Development, 1436.5.2 Middle Development, 1446.5.3 Late Development, 148

CONTENTS ix

6.6 Special Design Issues, 151

6.6.1 Placebos, 151

6.6.2 Equivalence or Noninferiority, 154

6.6.3 Nonuniformity of Effect, 154

6.6.4 Randomized Discontinuation, 155

6.6.5 Hybrid Designs May Be Needed for Resolving SpecialQuestions, 156

6.6.6 Clinical Trials Cannot Meet Some Objectives, 1566.7 Importance of the Protocol Document, 157

6.7.1 Protocols Have Many Functions, 158

6.7.2 Deviations from Protocol Specifications Are Common, 159

6.7.3 Protocols Are Structured, Logical, and Complete, 1606.8 Summary, 1646.9 Questions for Discussion, 165

7 Random Error and Bias 167

7.1 Introduction, 1677.2 Random Error, 169

7.2.1 Hypothesis Tests versus Significance Tests, 169

7.2.2 Hypothesis Tests Are Subject to Two Types of RandomError, 170

7.2.3 Type I Errors Are Relatively Easy to Control, 171

7.2.4 The Properties of Confidence Intervals Are Similar, 171

7.2.5 Using a One- or Two-Sided Hypothesis Test Is Not theRight Question, 172

7.2.6 P -Values Quantify the Type I Error, 173

7.2.7 Type II Errors Depend on the Clinical Difference ofInterest, 173

7.2.8 Post hoc Power Calculations Are not Helpful, 1757.3 Clinical Biases, 176

7.3.1 Relative Size of Random Error and Bias Is Important, 176

7.3.2 Bias Arises from Numerous Sources, 176

7.3.3 Controlling Structural Bias Is Conceptually Simple, 1797.4 Statistical Bias, 182

7.4.1 Some Statistical Bias Can Be Corrected, 183

7.4.2 Unbiasedness Is Not the Only Desirable Attribute of anEstimator, 183


8 Objectives and Outcomes 187


x CONTENTS

8.2 Objectives, 1888.2.1 Estimation Is the Most Common Objective, 1888.2.2 Selection Can Also Be an Objective, 1898.2.3 Objectives Require Various Scales of Measurement, 189

8.3 Outcomes, 1908.3.1 Mixed Outcomes and Predictors, 1908.3.2 Criteria for Evaluating Outcomes, 1918.3.3 Prefer “Hard” or Objective Outcomes, 1918.3.4 Outcomes Can Be Quantitative or Qualitative, 1928.3.5 Measures Are Useful and Efficient Outcomes, 1928.3.6 Some Outcomes Are Summarized as Counts, 1938.3.7 Ordered Categories Are Commonly Used for Severity or

Toxicity, 1938.3.8 Unordered Categories Are Sometimes Used, 1938.3.9 Dichotomies Are Simple Summaries, 1948.3.10 Event Times May Be Censored, 1948.3.11 Event Time Data Require Two Numerical Values, 1968.3.12 Censoring and Lost to Follow-up Are Not the Same, 1978.3.13 Survival and Disease Progression, 1988.3.14 Composite Outcomes Instead of Censoring, 1998.3.15 Waiting for Good Events Complicates Censoring, 199

8.4 Surrogate Outcomes, 2008.4.1 Surrogate Outcomes Are Disease-Specific, 2018.4.2 Surrogate Outcomes Can Make Trials More Efficient, 2048.4.3 Surrogate Outcomes Have Significant Limitations, 205

8.5 Some Special Endpoints, 2078.5.1 Repeated Measurements Are not Common in Clinical Tri-

als, 2078.5.2 Patient Reported Outcomes, 207


9 Translational Clinical Trials 211

9.1 Introduction, 2119.1.1 Translational Setting and Outcomes, 2129.1.2 Character and Definition, 2139.1.3 Small Does Not Mean Translational, 214

9.2 Information from Translational Trials, 2149.2.1 Parameter Uncertainty versus Outcome Uncertainty, 2149.2.2 Entropy, 2159.2.3 Empirical Entropy Is Biased, 2169.2.4 Variance, 2169.2.5 Sample Size for Translational Trials, 218

CONTENTS xi


10 Dose-Finding Designs 223

10.1 Introduction, 22310.2 Principles, 224

10.2.1 What Does “Phase I” Mean?, 22410.2.2 Distinguish Dose–Safety from Dose–Efficacy, 22610.2.3 Dose Optimality Is a Design Definition, 22610.2.4 The General Dose-Finding Problem Is Unsolved, 22710.2.5 Unavoidable Subjectivity, 22810.2.6 Sample Size Is an Outcome of Dose-Finding Studies, 22910.2.7 Idealized Dose-Finding Design, 229

10.3 Fibonacci and Related Dose-Ranging, 23010.3.1 Some Historical Designs, 23110.3.2 Typical Design, 23110.3.3 Operating Characteristics Can Be Calculated, 23210.3.4 Modifications, Strengths, and Weaknesses, 234

10.4 Designs Used for Dose-Finding, 23610.4.1 Mathematical Models Facilitate Inferences, 23610.4.2 Continual Reassessment Method, 23710.4.3 Pharmacokinetic Measurements Might Be Used to Improve

CRM Dose Escalations, 24010.4.4 The CRM Is an Attractive Design to Criticize, 24110.4.5 CRM Example, 24110.4.6 Can Randomization Be Used in Phase I or TM Trials?, 24210.4.7 Phase I Data Have Other Uses, 242

10.5 More General Dose-Finding Issues, 24210.5.1 Dose-Finding Is Not Always One Dimensional, 24310.5.2 Dual Dose-Finding, 24410.5.3 Optimizing Safety and Efficacy Jointly, 247


11 Sample Size and Power 251

11.1 Introduction, 25111.2 Principles, 252

11.2.1 What Is Precision?, 25311.2.2 What Is Power?, 25311.2.3 What Is Evidence?, 25411.2.4 Sample Size and Power Calculations Are Approximations, 25511.2.5 The Relationship between Power/Precision and Sample Size

Is Quadratic, 256

xii CONTENTS

11.3 Early Developmental Trials, 25611.3.1 Translational Trials, 25611.3.2 Dose-Finding Trials, 258

11.4 Safety and Activity Studies, 25811.4.1 Simple SA Designs Can Use Fixed Sample Size, 25911.4.2 Exact Binomial Confidence Limits Are Helpful, 26011.4.3 Bayesian Binomial Confidence Intervals, 26311.4.4 A Bayesian Approach Can Use Prior Information, 26411.4.5 Likelihood-Based Approach for Proportions, 26611.4.6 Confidence Intervals for a Mean Provide a Sample Size

Approach, 26711.4.7 Confidence Intervals for Event Rates Can Determine

Sample Size, 26811.4.8 Likelihood-Based Approach for Event Rates, 27111.4.9 Ineffective or Unsafe Treatments Should Be Discarded

Early, 27111.4.10 Two-Stage Designs Are Efficient, 27211.4.11 Randomized SA Trials, 273

11.5 Comparative Trials, 27711.5.1 How to Choose Type I and II Error Rates, 27711.5.2 Comparisons Using the t-Test Are a Good Learning

Example, 27811.5.3 Likelihood-Based Approach, 28011.5.4 Dichotomous Responses Are More Complex, 28211.5.5 Hazard Comparisons Yield Similar Equations, 28311.5.6 Parametric and Nonparametric Equations Are Connected, 28611.5.7 Accommodating Unbalanced Treatment Assignments, 28611.5.8 A Simple Accrual Model Can Also Be Incorporated, 28811.5.9 Noninferiority, 290

11.6 ES Trials, 29511.6.1 Model Rare Events with the Poisson Distribution, 29511.6.2 Likelihood Approach for Poisson Rates, 296

11.7 Other Considerations, 29711.7.1 Cluster Randomization Requires Increased Sample Size, 29711.7.2 Simple Cost Optimization, 29811.7.3 Increase the Sample Size for Nonadherence, 29911.7.4 Simulated Lifetables Can Be a Simple Design Tool, 30111.7.5 Sample Size for Prognostic Factor Studies, 30211.7.6 Computer Programs Simplify Calculations, 30311.7.7 Simulation Is a Powerful and Flexible Design Alternative, 30411.7.8 Power Curves are Sigmoid Shaped, 304


CONTENTS xiii

12 The Study Cohort 309

12.1 Introduction, 30912.2 Defining the Study Cohort, 310

12.2.1 Active Sampling or Enrichment, 31012.2.2 Participation May Select Subjects with Better Prognosis, 31112.2.3 Define the Study Population Using Eligibility and

Exclusion Criteria, 31412.2.4 Quantitative Selection Criteria versus False Precision, 31612.2.5 Comparative Trials Are Not Sensitive to Selection, 317

12.3 Anticipating Accrual, 31812.3.1 Using a Run-in Period, 31812.3.2 Estimate Accrual Quantitatively, 319

12.4 Inclusiveness, Representation, and Interactions, 32212.4.1 Inclusiveness Is a Worthy Goal, 32212.4.2 Barriers Can Hinder Trial Participation, 32212.4.3 Efficacy versus Effectiveness Trials, 32312.4.4 Representation: Politics Blunders into Science, 324


13 Treatment Allocation 331

13.1 Introduction, 33113.2 Randomization, 332

13.2.1 Randomization Controls the Influence of Unknown Fac-tors, 333

13.2.2 Haphazard Assignments Are Not Random, 33513.2.3 Simple Randomization Can Yield Imbalances, 335

13.3 Constrained Randomization, 33613.3.1 Blocking Improves Balance, 33613.3.2 Blocking and Stratifying Balances Prognostic Factors, 33713.3.3 Other Considerations Regarding Blocking, 339

13.4 Adaptive Allocation, 34013.4.1 Urn Designs Also Improve Balance, 34013.4.2 Minimization Yields Tight Balance, 34113.4.3 Play the Winner, 342

13.5 Other Issues Regarding Randomization, 34313.5.1 Administration of the Randomization, 34313.5.2 Computers Generate Pseudorandom Numbers, 34513.5.3 Randomization Justifies Type I Errors, 345

13.6 Unequal Treatment Allocation, 34913.6.1 Subsets May Be of Interest, 35013.6.2 Treatments May Differ Greatly in Cost, 35013.6.3 Variances May Be Different, 350

xiv CONTENTS

13.7 Randomization before Consent, 35113.8 Summary, 35213.9 Questions for Discussion, 352

14 Treatment Effects Monitoring 355


14.1.1 Motives for Monitoring, 356

14.1.2 Components of Responsible Monitoring, 357

14.1.3 Trials Can Be Stopped for a Variety of Reasons, 357

14.1.4 There Is Tension in the Decision to Stop, 35814.2 Administrative Issues in Trial Monitoring, 359

14.2.1 Monitoring of Single-Center Studies Relies on PeriodicInvestigator Reporting, 360

14.2.2 Composition and Organization of the TEMC, 361

14.2.3 Complete Objectivity Is Not Ethical, 36414.3 Organizational Issues Related to Data, 366

14.3.1 The TEMC Assesses Baseline Comparability, 366

14.3.2 The TEMC Reviews Accrual and Expected Time to StudyCompletion, 367

14.3.3 Timeliness of Data and Reporting Lags, 367

14.3.4 Data Quality Is a Major Focus of the TEMC, 368

14.3.5 The TEMC Reviews Safety and Toxicity Data, 368

14.3.6 Efficacy Differences Are Assessed by the TEMC, 369

14.3.7 The TEMC Should Address a Few Practical QuestionsSpecifically, 369

14.3.8 The TEMC Mechanism Has Potential Weaknesses, 37114.4 Statistical Methods for Monitoring, 371

14.4.1 There Are Several Approaches to Evaluating IncompleteEvidence, 371

14.4.2 Likelihood Methods, 373

14.4.3 Bayesian Methods, 378

14.4.4 Decision-Theoretic Methods, 381

14.4.5 Frequentist Methods, 381

14.4.6 Other Monitoring Tools, 387

14.4.7 Some Software, 39214.5 Summary, 39214.6 Questions for Discussion, 393

15 Counting Subjects and Events 395

15.1 Introduction, 39515.2 Nature of Some Specific Data Imperfections, 396

15.2.1 Evaluability Criteria Are a Methodologic Error, 397

CONTENTS xv

15.2.2 Statistical Methods Can Cope with Some Types of MissingData, 398

15.2.3 Protocol Nonadherence Is Common, 40015.3 Treatment Nonadherence, 402

15.3.1 Intention to Treat Is a Policy of Inclusion, 40215.3.2 Coronary Drug Project Results Illustrate the Pitfalls of

Exclusions based on Nonadherence, 40315.3.3 Statistical Studies Support the ITT Approach, 40315.3.4 Trials Can Be Viewed as Tests of Treatment Policy, 40415.3.5 ITT Analyses Cannot Always Be Applied, 40415.3.6 Trial Inferences Depend on the Experimental Design, 406


16 Estimating Clinical Effects 409

16.1 Introduction, 40916.1.1 Structure Aids Data Interpretation, 41016.1.2 Estimates of Risk Are Natural and Useful, 411

16.2 Dose-Finding and PK Trials, 41216.2.1 Pharmacokinetic Models Are Essential for Analyzing DF

Trials, 41216.2.2 A Two-Compartment Model Is Simple but Realistic, 41316.2.3 PK Models Are Used by “Model Fitting”, 416

16.3 SA Studies, 41716.3.1 Mesothelioma Clinical Trial Example, 41716.3.2 Summarize Risk for Dichotomous Factors, 41816.3.3 Nonparametric Estimates of Survival

Are Robust, 42016.3.4 Parametric (Exponential) Summaries of Survival Are Effi-

cient, 42116.4 Comparative Efficacy Trials (Phase III), 423

16.4.1 Examples of CTE Trials Used in This Section, 42416.4.2 Continuous Measures Estimate Treatment Differences, 42616.4.3 Baseline Measurements Can Increase Precision, 42616.4.4 Nonparametric Survival Comparisons, 42716.4.5 Risk (Hazard) Ratios and Confidence Intervals Are Clini-

cally Useful Data Summaries, 42916.4.6 Statistical Models Are Helpful Tools, 43116.4.7 P -Values Do Not Measure Evidence, 432

16.5 Strength of Evidence through Support Intervals, 43416.5.1 Support Intervals Are Based on the Likelihood Function, 43416.5.2 Support Intervals Can Be Used with Any Outcome, 435

16.6 Special Methods of Analysis, 436

xvi CONTENTS

16.6.1 The Bootstrap Is Based on Resampling, 43716.6.2 Some Clinical Questions Require Other Special Methods

of Analysis, 43816.7 Exploratory or Hypothesis-Generating Analyses, 442

16.7.1 Clinical Trial Data Lend Themselves to Exploratory Anal-yses, 442

16.7.2 Multiple Tests Multiply Type I Errors, 44216.7.3 Kinds of Multiplicity, 44316.7.4 Subgroup Analyses Are Error Prone, 443


17 Prognostic Factor Analyses 453

17.1 Introduction, 45317.1.1 Studying Prognostic Factors Is Broadly Useful, 45417.1.2 Prognostic Factors Can Be Constant or Time-Varying, 455

17.2 Model-Based Methods, 45617.2.1 Models Combine Theory and Data, 45617.2.2 The Scale of Measurements (Coding) May Be Important, 45717.2.3 Use Flexible Covariate Models, 45717.2.4 Building Parsimonious Models Is the Next Step, 45917.2.5 Incompletely Specified Models May Yield Biased Estimates, 46417.2.6 Study Second-Order Effects (Interactions), 46517.2.7 PFAs Can Help Describe Risk Groups, 46617.2.8 Power and Sample Size for PFAs, 470

17.3 Adjusted Analyses of Comparative Trials, 47017.3.1 What Should We Adjust For?, 47117.3.2 What Can Happen?, 472

17.4 Non–Model-Based Methods for PFAs, 47517.4.1 Recursive Partitioning Uses Dichotomies, 47517.4.2 Neural Networks Are Used for Pattern Recognition, 476


18 Reporting and Authorship 479

18.1 Introduction, 47918.2 General Issues in Reporting, 480

18.2.1 Uniformity of Reporting Can Improve Comprehension, 48118.2.2 Quality of the Literature, 48218.2.3 Peer Review Is the Only Game in Town, 48218.2.4 Publication Bias Can Distort Impressions Based on the

Literature, 48318.3 Clinical Trial Reports, 484

CONTENTS xvii

18.3.1 General Considerations, 48518.3.2 Employ a Complete Outline for CTE Reporting, 490

18.4 Authorship, 49418.4.1 Inclusion and Ordering, 49518.4.2 Responsibility of Authorship, 49618.4.3 Authorship Models, 49618.4.4 Some Other Practicalities, 498

18.5 Alternative Ways to Disseminate Results, 49918.6 Summary, 49918.7 Questions for Discussion, 500

19 Factorial Designs 501

19.1 Introduction, 50119.2 Characteristics of Factorial Designs, 502

19.2.1 Interactions or Efficiency, but Not Both Simultaneously, 50219.2.2 Factorial Designs Are Defined by Their Structure, 50219.2.3 Factorial Designs Can Be Made Efficient, 504

19.3 Treatment Interactions, 50519.3.1 Factorial Designs Are the Only Way to Study Interactions, 50519.3.2 Interactions Depend on the Scale of Measurement, 50719.3.3 The Interpretation of Main Effects Depends on Interac-

tions, 50719.3.4 Analyses Can Employ Linear Models, 508

19.4 Examples of Factorial Designs, 50919.5 Partial, Fractional, and Incomplete Factorials, 511

19.5.1 Use Partial Factorial Designs When Interactions Are Absent, 51119.5.2 Incomplete Designs Present Special Problems, 512


20 Crossover Designs 515

20.1 Introduction, 51520.1.1 Other Ways of Giving Multiple Treatments Are Not

Crossovers, 51620.1.2 Treatment Periods May Be Randomly Assigned, 516

20.2 Advantages and Disadvantages, 51720.2.1 Crossover Designs Can Increase Precision, 51720.2.2 A Crossover Design Might Improve Recruitment, 51820.2.3 Carryover Effects Are a Potential Problem, 51820.2.4 Dropouts Have Strong Effects, 51920.2.5 Analysis Is More Complex Than Parallel-Groups Designs, 51920.2.6 Prerequisites Are Needed to Apply Crossover Designs, 520

20.3 Analysis, 520

xviii CONTENTS

20.3.1 Analysis Can Be Based on a Cell Means Model, 52120.3.2 Other Issues in Analysis, 52520.3.3 Classic Case Study, 525


21 Meta-Analyses 529

21.1 Introduction, 52921.1.1 Meta-Analyses Formalize Synthesis and Increase Precision, 530

21.2 A Sketch of Meta-Analysis Methods, 53121.2.1 Meta-Analysis Necessitates Prerequisites, 53121.2.2 Many Studies Are Potentially Relevant, 53221.2.3 Select Studies, 53321.2.4 Plan the Statistical Analysis, 53321.2.5 Summarize the Data Using Observed and Expected, 534

21.3 Other Issues, 53621.3.1 Meta-Analyses Have Practical and Theoretical Limitations, 53621.3.2 Meta-Analysis Has Taught Useful Lessons, 537


22 Misconduct and Fraud in Clinical Research 539

22.1 Introduction, 53922.1.1 Integrity and Accountability Are Critically Important, 54022.1.2 Fraud and Misconduct Are Difficult to Define, 541

22.2 Research Practices, 54422.2.1 Misconduct Has Been Uncommon, 54522.2.2 Causes of Misconduct, 546

22.3 General Approach to Allegations of Misconduct, 54722.3.1 Government, 54722.3.2 Institutions, 54922.3.3 Problem Areas, 550

22.4 Characteristics of Some Misconduct Cases, 55122.4.1 Darsee Case, 55122.4.2 Poisson (NSABP) Case, 55322.4.3 Two Recent Cases from Germany, 556

22.5 Lessons, 55722.5.1 Recognizing Fraud or Misconduct, 55722.5.2 Misconduct Cases Yield Other Lessons, 559

22.6 Clinical Investigators’ Responsibilities, 56022.6.1 General Responsibilities, 56022.6.2 Additional Responsibilities Related to INDs, 56122.6.3 Sponsor Responsibilities, 562

CONTENTS xix


Appendices

A Data and Programs 565

A.1 Introduction, 565A.2 Data, 566A.3 Design Programs, 566

A.3.1 Power and Sample Size Program, 566

A.3.2 Blocked Stratified Randomization, 567

A.3.3 Continual Reassessment Method, 567A.4 Mathematica Code, 567

B Notation and Terminology 569

B.1 Introduction, 569B.2 Notation, 569

B.2.1 Greek Letters, 570

B.2.2 Roman Letters, 571

B.2.3 Other Symbols, 572B.3 Terminology and Concepts, 572

C Abbreviations 587

D Nuremberg Code 591

E Declaration of Helsinki 593

E.1 Introduction, 593E.2 Basic Principles for All Medical Research, 594

F NCI Data and Safety Monitoring Policy 599

F.1 Introduction, 599F.2 Responsibility for Data and Safety Monitoring, 599F.3 Requirement for Data and Safety Monitoring Boards, 600F.4 Responsibilities of the DSMB, 600F.5 Membership, 600F.6 Meetings, 601F.7 Recommendations from the DSMB, 601F.8 Release of Outcome Data, 602F.9 Confidentiality Procedures, 602F.10 Conflict of Interest, 602

xx CONTENTS

G NIH Data and Safety Monitoring Policy 605

G.1 Background, 605G.2 Principles of Monitoring Data and Safety, 606G.3 Practical and Implementation Issues: Oversight of Monitoring, 606G.4 Institutes and Centers Responsibilities, 606G.5 Performance of Data and Safety Monitoring, 607G.6 Examples of Monitoring Operations, 608

H Royal Statistical Society Code of Conduct 609

H.1 Introduction, 609H.2 Constitutional Authority, 609H.3 Rules of Professional Conduct, 609

H.3.1 The Public Interest, 610H.3.2 Duty to Employers and Clients, 610H.3.3 Duty to the Profession, 610H.3.4 Disciplinary Procedures, 611

Bibliography 613

Author Index 659

Subject Index 675

PREFACE

A respectable period of time has passed since the first edition of this book, during whichseveral pressures have necessitated a second edition. Most important, there were theinadvertent errors needing correction. The field has also had some significant changes,not so much in methodology perhaps as in context, regulation, and the like. I can saysome things more clearly now than previously because many issues are better definedor I care more (or less) about how the discussion will be received.

I have added much new material covering some gaps (and therefore some newmistakes), but always hoping to make learning easier. The result is too much to coverin the period that I usually teach—one academic quarter. It may be appropriate for asemester course. Many students tell me that they consult this book as a reference, sothe extra material should ultimately be useful.

Many colleagues have been kind enough to give their valuable time to review draftsof chapters, despite apparently violating Nabokov’s advice: “Only ambitious nonenti-ties and hearty mediocrities exhibit their rough drafts. It is like passing around samplesof one’s sputum.” Nevertheless, I am grateful to Elizabeth Garrett-Mayer, Ph.D., Bon-nie Piantadosi, M.S.W, M.H.S., Anne Piantadosi, Irene Roach, Pamela Scott, Ph.D.,Gail Weinmann, M.D., and Xiaobu Ye, M.D., M.S. for such help. Chris Szekely,Ph.D. reviewed many chapters and references in detail. Sean Roach, M.L.S. and AlisaMoore provided valuable assistance with references. Alla Guseynova, M.S. reviewedand corrected the computational code for the book.

As usual, students in my class Design and Analysis of Clinical Trials offered throughthe Hopkins Department of Biostatistics and the Graduate Training Program in ClinicalInvestigation provide the best motivation for writing this book by virtue of their livelydiscussion and questions. I would also like to thank the faculties, students, and spon-sors from the AACR/ASCO Vail Workshop, as well as the sister workshops, FECS inFlims, Switzerland, and ACORD in Cairns, Australia, who have provided many prac-tical questions and examples for interesting clinical trial designs over the years. Suchteaching venues require conciseness and precision from a clinical trialist, and illustratethe very heart of collaborative research.

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xxii PREFACE

Finally there are two institutional venues that have helped motivate and shape mywriting regarding clinical trials. One is the Protocol Review and Monitoring Committeein the Cancer Center, a scientific review forum on which I have served for manyyears. The second is the Institutional Review Board, on which I have more recentlybegun to serve. My colleagues in both of these settings encourage and take a careful,constructive, and detailed view of a multitude of diverse trials, and have taught me agreat deal.

Steven PiantadosiBaltimore, Maryland, 2005

PREFACE xxiii

Books are fatal: they are the curse of the human race. Nine-tenths of existing books arenonsense, and the clever books are the refutation of that nonsense. The greatest misfortunethat ever befell man was the invention of printing. [Benjamin Disraeli]

Writing is an adventure. To begin with, it is a toy and an amusement. Then it becomesa mistress, then it becomes a master, then it becomes a tyrant. The last phase is that justas you are about to be reconciled to your servitude, you kill the monster and fling him tothe public. [Winston Churchill]

Writing is easy; all you do is sit staring at a blank sheet of paper until the drops of bloodform on your forehead. [Gene Fowler]

PREFACE TO THE FIRST EDITION

In recent years a great deal has been written about clinical trials and closely related areasof biostatistics, biomathematics, biometry, epidemiology, and clinical epidemiology.The motive for writing this book is that there still seems to be a need among bothphysicians and biostatisticians for direct, relevant accounts of basic statistical methodsin clinical trials. The need for both trialists and clinical investigators to learn about goodmethodology is particularly acute in oncology, where investigators search for treatmentadvances of great clinical importance, but modest size relative to the variability and biaswhich characterize studies of human disease. A similar need with the same motivationexists in many other diseases.

On the medical side of clinical trials, the last few years have seen a sharpened focuson training of clinical investigators in research methods. Training efforts have rangedfrom short intensive courses to research fellowships lasting years and culminating ina postgraduate degree. The evolution of teaching appears to be toward defining aspecialty in clinical research. The material in this book should be of interest to thosewho take this path. The technical subjects may seem difficult at first, but the clinicianshould soon become comfortable with them.

On the biostatistical side of clinical trials, there has been a near explosion of methodsin recent years. However, this is not a book on statistical theory. Readers with agood foundation in biostatistics should find the technical subjects practical and quiteaccessible. It is my hope that such students will see some cohesiveness to the field, fillin gaps in their knowledge, and be able to explore areas such as ethics and misconductthat are important to clinical trials.

There are some popular perceptions about clinical trials to which this book does notsubscribe. For example, some widely used terminology regarding trials is unhelpfuland I have attempted to counteract it by proposing alternatives. Also noncomparativetrial designs (e.g., early developmental studies) are often inappropriately excluded fromdiscussions of methods. I have tried to present concepts that unify all designed studiesrather than ideas that artificially distinguish them. Dealing with pharmacokinetic-based

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xxvi PREFACE TO THE FIRST EDITION

designs tends to complicate some of the mathematics, but the concepts are essentialfor understanding these trials.

The book is intended to provide at least enough material for the core of a half-semester course on clinical trials. In research settings where trials are used, the audiencefor such a course will likely have varying skills and directions. However, with abackground of basic biostatistics, an introductory course in clinical trials or researchmethods, and appropriate didactic discussion, the material presented here should beuseful to a heterogeneous group of students.

Many individuals have contributed to this book in indirect, but significant ways.I am reminded of two colleagues, now deceased, who helped to shape my thinkingabout clinical trials. David P. Byar, M.D. nurtured my early academic and quantitativeinterest in clinical trials in the early 1980s at the National Institutes of Health. AfterI joined the Johns Hopkins School of Medicine, Brigid G. Leventhal, M.D. showedme a mature, compassionate, and rigorous view of clinical trials from a practitioner’sperspective. I hope the thoughts in this book reflect some of the good attitudes andvalues of these fine scholars.

Other colleagues have taught me many lessons regarding clinical trials through theirwritings, lectures, conversations, and willingness to answer many questions. I wouldparticularly like to thank Mitchell H. Gail, M.D., Ph.D. and Curtis L. Meinert, Ph.D. formuch valuable advice and good example over the years. One of the most worthwhileexperiences that a trial methodologist can have is to review and influence the designsof clinical trials while they are being developed. My colleagues at Johns Hopkins havecooperated in this regard through the Oncology Center’s Clinical Research Committee,especially Hayden Braine, M.D., who has chaired the Committee wisely for many years.

Through long-standing collaborations with the Lung Cancer Study Group, I metmany clinical scholars with much to say and teach about trials. I would like to thankthem, especially E. Carmack Holmes, M.D., John C. Ruckdeschel, M.D., and RobertGinzberg, M.D. for showing me a model of interdisciplinary collaboration and friend-ship, which continued to outlive the financial arrangements. Recent collaborations withcolleagues in the New Approaches to Brain Tumor Therapy Consortium have alsoenhanced my appreciation and understanding of early developmental trials.

In recent months many colleagues have assisted me by reading and offering com-ments on drafts of the chapters that follow. For this help, I would like to thank LinaAsmar, Ph.D., Tatiana Barkova, Ph.D., Jeanne DeJoseph, Ph.D., C.N.M., R.N., SuzanneDibble, D.N.Sc., R.N., James Grizzle, Ph.D., Curt Meinert, Ph.D., Mitch Gail, M.D.,Ph.D., Barbara Hawkins, Ph.D., Steven Goodman, M.D., Ph.D., Cheryl Enger, Ph.D.,Guanghan Liu, Ph.D., J. Jack Lee, Ph.D., Claudia Moy, Ph.D., John O’Quigley, Ph.D.,Thomas F. Pajak, Ph.D., Charles Rohde, Ph.D., Barbara Starklauf, M.A.S., Manel C.Wijesinha, Ph.D., and Marianna Zahurak, M.S. Many of the good points belong tothem—the errors are mine.

Students in my classes on the Design and Analysis of Clinical Trials and Design ofExperiments at the Johns Hopkins School of Hygiene and Public Health have con-tributed to this book by working with drafts, making helpful comments, workingproblems, or just discussing particular points. I would especially like to thank MariaDeloria, Kathleen Weeks, Ling-Yu Ruan, and Jeffrey Blume for their input. HelenCromwell and Patty Hubbard have provided a great deal of technical assistance withthe preparation of the manuscript. Gary D. Knott, Ph.D., Barry J. Bunow, Ph.D., andthe staff at Civilized Software, Bethesda, Maryland (www.civilized.com) furnished me

PREFACE TO THE FIRST EDITION xxvii

with MLAB software, without which many tasks in the following pages would bedifficult.

This book was produced in LATEX using Scientific Workplace version 2.5. I amindebted to Kathy Watt of TCI Software Research in Las Cruces, New Mexico,(www.tcisoft.com) for assistance in preparing the style. A special thanks goes to IreneRoach for editing an early draft of the manuscript and to Sean Roach for collectingand translating hard-to-find references.

It is not possible to write a book without stealing a large amount of time from one’sfamily. Bonnie, Anne L., and Steven T. not only permitted this to happen but also weresupportive, helpful, and understood why I felt it was necessary. I am most grateful tothem for their patience and understanding throughout this project.

Steven PiantadosiBaltimore, Maryland,

March 1997

[wiley series in probability and statistics] clinical trials (a methodologic perspective) ||...

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