Clinical Trials
A Methodologic Perspective
Wiley Series in Probability and Statistics
3. Edition October 2017
928 Pages, Hardcover
Wiley & Sons Ltd
ISBN:
978-1-118-95920-6
John Wiley & Sons
Further versions
Presents elements of clinical trial methods that are essential in planning, designing, conducting, analyzing, and interpreting clinical trials with the goal of improving the evidence derived from these important studies
This Third Edition builds on the text's reputation as a straightforward, detailed, and authoritative presentation of quantitative methods for clinical trials. Readers will encounter the principles of design for various types of clinical trials, and are then skillfully guided through the complete process of planning the experiment, assembling a study cohort, assessing data, and reporting results. Throughout the process, the author alerts readers to problems that may arise during the course of the trial and provides common sense solutions. All stages of therapeutic development are discussed in detail, and the methods are not restricted to a single clinical application area.
The authors bases current revisions and updates on his own experience, classroom instruction, and feedback from teachers and medical and statistical professionals involved in clinical trials. The Third Edition greatly expands its coverage, ranging from statistical principles to new and provocative topics, including alternative medicine and ethics, middle development, comparative studies, and adaptive designs. At the same time, it offers more pragmatic advice for issues such as selecting outcomes, sample size, analysis, reporting, and handling allegations of misconduct. Readers familiar with the First and Second Editions will discover revamped exercise sets; an updated and extensive reference section; new material on endpoints and the developmental pipeline, among others; and revisions of numerous sections.
In addition, this book:
* Features accessible and broad coverage of statistical design methods--the crucial building blocks of clinical trials and medical research -- now complete with new chapters on overall development, middle development, comparative studies, and adaptive designs
* Teaches readers to design clinical trials that produce valid qualitative results backed by rigorous statistical methods
* Contains an introduction and summary in each chapter to reinforce key points
* Includes discussion questions to stimulate critical thinking and help readers understand how they can apply their newfound knowledge
* Provides extensive references to direct readers to the most recent literature, and there are numerous new or revised exercises throughout the book
Clinical Trials: A Methodologic Perspective, Third Edition is a textbook accessible to advanced undergraduate students in the quantitative sciences, graduate students in public health and the life sciences, physicians training in clinical research methods, and biostatisticians and epidemiologists.
Steven Piantadosi, MD, PhD, is the Phase One Foundation Distinguished Chair and Director of the Samuel Oschin Cancer Institute, and Professor of Medicine at Cedars-Sinai Medical Center in Los Angeles, California. Dr. Piantadosi is one of the world's leading experts in the design and analysis of clinical trials for cancer research. He has taught clinical trials methods extensively in formal courses and short venues. He has advised numerous academic programs and collaborations nationally regarding clinical trial design and conduct, and has served on external advisory boards for the National Institutes of Health and other prominent cancer programs and centers. The author of more than 260 peer-reviewed scientific articles, Dr. Piantadosi has published extensively on research results, clinical applications, and trial methodology. While his papers have contributed to many areas of oncology, he has also collaborated on diverse studies outside oncology including lung disease and degenerative neurological disease.
Preface to the Third Edition xxv
About the Companion Website xxviii
1 Preliminaries 1
1.1 Introduction, 1
1.2 Audiences, 2
1.3 Scope, 3
1.4 Other Sources of Knowledge, 5
1.5 Notation and Terminology, 6
1.5.1 Clinical Trial Terminology, 7
1.5.2 Drug Development Traditionally Recognizes Four Trial Design Types, 7
1.5.3 Descriptive Terminology Is Better, 8
1.6 Examples, Data, and Programs, 9
1.7 Summary, 9
2 Clinical Trials as Research 10
2.1 Introduction, 10
2.2 Research, 13
2.2.1 What Is Research?, 13
2.2.2 Clinical Reasoning Is Based on the Case History, 14
2.2.3 Statistical Reasoning Emphasizes Inference Based on Designed Data Production, 16
2.2.4 Clinical and Statistical Reasoning Converge in Research, 17
2.3 Defining Clinical Trials, 19
2.3.1 Mixing of Clinical and Statistical Reasoning Is Recent, 19
2.3.2 Clinical Trials Are Rigorously Defined, 21
2.3.3 Theory and Data, 22
2.3.4 Experiments Can Be Misunderstood, 23
2.3.5 Clinical Trials and the Frankenstein Myth, 25
2.3.6 Cavia porcellus, 26
2.3.7 Clinical Trials as Science, 26
2.3.8 Trials and Statistical Methods Fit within a Spectrum of Clinical Research, 28
2.4 Practicalities of Usage, 29
2.4.1 Predicates for a Trial, 29
2.4.2 Trials Can Provide Confirmatory Evidence, 29
2.4.3 Clinical Trials Are Reliable Albeit Unwieldy and Messy, 30
2.4.4 Trials Are Difficult to Apply in Some Circumstances, 31
2.4.5 Randomized Studies Can Be Initiated Early, 32
2.4.6 What Can I learn from = 20?, 33
2.5 Nonexperimental Designs, 35
2.5.1 Other Methods Are Valid forMaking Some Clinical Inferences, 35
2.5.2 Some Specific Nonexperimental Designs, 38
2.5.3 Causal Relationships, 40
2.5.4 Will Genetic Determinism Replace Design?, 41
2.6 Summary, 41
2.7 Questions for Discussion, 41
3 Why Clinical Trials Are Ethical 43
3.1 Introduction, 43
3.1.1 Science and Ethics Share Objectives, 44
3.1.2 Equipoise and Uncertainty, 46
3.2 Duality, 47
3.2.1 Clinical Trials Sharpen, But Do Not Create, Duality, 47
3.2.2 A Gene Therapy Tragedy Illustrates Duality, 48
3.2.3 Research and Practice Are Convergent, 48
3.2.4 Hippocratic Tradition Does Not Proscribe Clinical Trials, 52
3.2.5 Physicians Always Have Multiple Roles, 54
3.3 Historically Derived Principles of Ethics, 57
3.3.1 Nuremberg Contributed an Awareness of the Worst Problems, 57
3.3.2 High-Profile Mistakes Were Made in the United States, 58
3.3.3 The Helsinki Declaration Was Widely Adopted, 58
3.3.4 Other International Guidelines Have Been Proposed, 61
3.3.5 Institutional Review Boards Provide Ethics Oversight, 62
3.3.6 Ethics Principles Relevant to Clinical Trials, 63
3.4 Contemporary Foundational Principles, 65
3.4.1 Collaborative Partnership, 66
3.4.2 Scientific Value, 66
3.4.3 Scientific Validity, 66
3.4.4 Fair Subject Selection, 67
3.4.5 Favorable Risk-Benefit, 67
3.4.6 Independent Review, 68
3.4.7 Informed Consent, 68
3.4.8 Respect for Subjects, 71
3.5 Methodologic Reflections, 72
3.5.1 Practice Based on Unproven Treatments Is Not Ethical, 72
3.5.2 Ethics Considerations Are Important Determinants of Design, 74
3.5.3 Specific Methods Have Justification, 75
3.6 Professional Conduct, 79
3.6.1 Advocacy, 79
3.6.2 Physician to Physician Communication Is Not Research, 81
3.6.3 Investigator Responsibilities, 82
3.6.4 Professional Ethics, 83
3.7 Summary, 85
3.8 Questions for Discussion, 86
4 Contexts for Clinical Trials 87
4.1 Introduction, 87
4.1.1 Clinical Trial Registries, 88
4.1.2 Public Perception Versus Science, 90
4.2 Drugs, 91
4.2.1 Are Drugs Special?, 92
4.2.2 Why Trials Are Used Extensively for Drugs, 93
4.3 Devices, 95
4.3.1 Use of Trials for Medical Devices, 95
4.3.2 Are Devices Different from Drugs?, 97
4.3.3 Case Study, 98
4.4 Prevention, 99
4.4.1 The Prevention versus Therapy Dichotomy Is Over-worked, 100
4.4.2 Vaccines and Biologicals, 101
4.4.3 Ebola 2014 and Beyond, 102
4.4.4 A Perspective on Risk-Benefit, 103
4.4.5 Methodology and Framework for Prevention Trials, 105
4.5 Complementary and Alternative Medicine, 106
4.5.1 Science Is the Study of Natural Phenomena, 108
4.5.2 Ignorance Is Important, 109
4.5.3 The Essential Paradox of CAM and Clinical Trials, 110
4.5.4 Why Trials Have Not Been Used Extensively in CAM, 111
4.5.5 Some Principles for Rigorous Evaluation, 113
4.5.6 Historic Examples, 115
4.6 Surgery and Skill-Dependent Therapies, 116
4.6.1 Why Trials Have Been Used Less Extensively in Surgery, 118
4.6.2 Reasons Why Some Surgical Therapies Require Less Rigorous Study Designs, 120
4.6.3 Sources of Variation, 121
4.6.4 Difficulties of Inference, 121
4.6.5 Control of Observer Bias Is Possible, 122
4.6.6 Illustrations from an Emphysema Surgery Trial, 124
4.7 A Brief View of Some Other Contexts, 130
4.7.1 Screening Trials, 130
4.7.2 Diagnostic Trials, 134
4.7.3 Radiation Therapy, 134
4.8 Summary, 135
4.9 Questions for Discussion, 136
5 Measurement 137
5.1 Introduction, 137
5.1.1 Types of Uncertainty, 138
5.2 Objectives, 140
5.2.1 Estimation Is The Most Common Objective, 141
5.2.2 Selection Can Also Be an Objective, 141
5.2.3 Objectives Require Various Scales of Measurement, 142
5.3 Measurement Design, 143
5.3.1 Mixed Outcomes and Predictors, 143
5.3.2 Criteria for Evaluating Outcomes, 144
5.3.3 Prefer Hard or Objective Outcomes, 145
5.3.4 Outcomes Can Be Quantitative or Qualitative, 146
5.3.5 Measures Are Useful and Efficient Outcomes, 146
5.3.6 Some Outcomes Are Summarized as Counts, 147
5.3.7 Ordered Categories Are Commonly Used for Severity or Toxicity, 147
5.3.8 Unordered Categories Are Sometimes Used, 148
5.3.9 Dichotomies Are Simple Summaries, 148
5.3.10 Measures of Risk, 149
5.3.11 Primary and Others, 153
5.3.12 Composites, 154
5.3.13 Event Times and Censoring, 155
5.3.14 Longitudinal Measures, 160
5.3.15 Central Review, 161
5.3.16 Patient Reported Outcomes, 161
5.4 Surrogate Outcomes, 162
5.4.1 Surrogate Outcomes Are Disease-Specific, 164
5.4.2 Surrogate Outcomes Can Make Trials More Efficient, 167
5.4.3 Surrogate Outcomes Have Significant Limitations, 168
5.5 Summary, 170
5.6 Questions for Discussion, 171
6 Random Error and Bias 172
6.1 Introduction, 172
6.1.1 The Effects of Random and Systematic Errors Are Distinct, 173
6.1.2 Hypothesis Tests versus Significance Tests, 174
6.1.3 Hypothesis Tests Are Subject to Two Types of Random Error, 175
6.1.4 Type I Errors Are Relatively Easy to Control, 176
6.1.5 The Properties of Confidence IntervalsAre Similar toHypothesis Tests, 176
6.1.6 Using a one- or two-sided hypothesis test is not the right question, 177
6.1.7 P-Values Quantify the Type I Error, 178
6.1.8 Type II Errors Depend on the Clinical Difference of Interest, 178
6.1.9 Post Hoc Power Calculations Are Useless, 180
6.2 Clinical Bias, 181
6.2.1 Relative Size of Random Error and Bias is Important, 182
6.2.2 Bias Arises from Numerous Sources, 182
6.2.3 Controlling Structural Bias is Conceptually Simple, 185
6.3 Statistical Bias, 188
6.3.1 Selection Bias, 188
6.3.2 Some Statistical Bias Can Be Corrected, 192
6.3.3 Unbiasedness is Not the Only Desirable Attribute of an Estimator, 192
6.4 Summary, 194
6.5 Questions for Discussion, 194
7 Statistical Perspectives 196
7.1 Introduction, 196
7.2 Differences in Statistical Perspectives, 197
7.2.1 Models and Parameters, 197
7.2.2 Philosophy of Inference Divides Statisticians, 198
7.2.3 Resolution, 199
7.2.4 Points of Agreement, 199
7.3 Frequentist, 202
7.3.1 Binomial Case Study, 203
7.3.2 Other Issues, 204
7.4 Bayesian, 204
7.4.1 Choice of a Prior Distribution Is a Source of Contention, 205
7.4.2 Binomial Case Study, 206
7.4.3 Bayesian Inference Is Different, 209
7.5 Likelihood, 210
7.5.1 Binomial Case Study, 211
7.5.2 Likelihood-Based Design, 211
7.6 Statistics Issues, 212
7.6.1 Perspective, 212
7.6.2 Statistical Procedures Are Not Standardized, 213
7.6.3 Practical Controversies Related to Statistics Exist, 214
7.7 Summary, 215
7.8 Questions for Discussion, 216
8 Experiment Design in Clinical Trials 217
8.1 Introduction, 217
8.2 Trials As Simple Experiment Designs, 218
8.2.1 Design Space Is Chaotic, 219
8.2.2 Design Is Critical for Inference, 220
8.2.3 The Question Drives the Design, 220
8.2.4 Design Depends on the Observation Model As Well As the
Biological Question, 221
8.2.5 Comparing Designs, 222
8.3 Goals of Experiment Design, 223
8.3.1 Control of Random Error and Bias Is the Goal, 223
8.3.2 Conceptual Simplicity Is Also a Goal, 223
8.3.3 Encapsulation of Subjectivity, 224
8.3.4 Leech Case Study, 225
8.4 Design Concepts, 225
8.4.1 The Foundations of Design Are Observation and Theory, 226
8.4.2 A Lesson from the Women's Health Initiative, 227
8.4.3 Experiments Use Three Components of Design, 229
8.5 Design Features, 230
8.5.1 Enrichment, 231
8.5.2 Replication, 232
8.5.3 Experimental and Observational Units, 232
8.5.4 Treatments and Factors, 233
8.5.5 Nesting, 233
8.5.6 Randomization, 234
8.5.7 Blocking, 234
8.5.8 Stratification, 235
8.5.9 Masking, 236
8.6 Special Design Issues, 237
8.6.1 Placebos, 237
8.6.2 Equivalence and Noninferiority, 240
8.6.3 Randomized Discontinuation, 241
8.6.4 Hybrid Designs May Be Needed for Resolving Special Questions, 242
8.6.5 Clinical Trials Cannot Meet Certain Objectives, 242
8.7 Importance of the Protocol Document, 244
8.7.1 Protocols Have Many Functions, 244
8.7.2 Deviations from Protocol Specifications are Common, 245
8.7.3 Protocols Are Structured, Logical, and Complete, 246
8.8 Summary, 252
8.9 Questions for Discussion, 253
9 The Trial Cohort 254
9.1 Introduction, 254
9.2 Cohort Definition and Selection, 255
9.2.1 Eligibility and Exclusions, 255
9.2.2 Active Sampling and Enrichment, 257
9.2.3 Participation may select subjects with better prognosis, 258
9.2.4 Quantitative Selection Criteria Versus False Precision, 262
9.2.5 Comparative Trials Are Not Sensitive to Selection, 263
9.3 Modeling Accrual, 264
9.3.1 Using a Run-In Period, 264
9.3.2 Estimate Accrual Quantitatively, 265
9.4 Inclusiveness, Representation, and Interactions, 267
9.4.1 Inclusiveness Is a Worthy Goal, 267
9.4.2 Barriers Can Hinder Trial Participation, 268
9.4.3 Efficacy versus Effectiveness Trials, 269
9.4.4 Representation: Politics Blunders into Science, 270
9.5 Summary, 275
9.6 Questions for Discussion, 275
10 Development Paradigms 277
10.1 Introduction, 277
10.1.1 Stages of Development, 278
10.1.2 Trial Design versus Development Design, 280
10.1.3 Companion Diagnostics in Cancer, 281
10.2 Pipeline Principles and Problems, 281
10.2.1 The Paradigm Is Not Linear, 282
10.2.2 Staging Allows Efficiency, 282
10.2.3 The Pipeline Impacts Study Design, 283
10.2.4 Specificity and Pressures Shape the Pipeline, 283
10.2.5 Problems with Trials, 284
10.2.6 Problems in the Pipeline, 286
10.3 A Simple Quantitative Pipeline, 286
10.3.1 Pipeline Operating Characteristics Can Be Derived, 286
10.3.2 Implications May Be Counterintuitive, 288
10.3.3 Optimization Yields Insights, 288
10.3.4 Overall Implications for the Pipeline, 291
10.4 Late Failures, 292
10.4.1 Generic Mistakes in Evaluating Evidence, 293
10.4.2 "Safety" Begets Efficacy Testing, 293
10.4.3 Pressure to Advance Ideas Is Unprecedented, 294
10.4.4 Scientists Believe Weird Things, 294
10.4.5 Confirmation Bias, 295
10.4.6 Many Biological Endpoints Are Neither Predictive nor Prognostic, 296
10.4.7 Disbelief Is Easier to Suspend Than Belief, 296
10.4.8 Publication Bias, 297
10.4.9 Intellectual Conflicts of Interest, 297
10.4.10 Many Preclinical Models Are Invalid, 298
10.4.11 Variation Despite Genomic Determinism, 299
10.4.12 Weak Evidence Is Likely to Mislead, 300
10.5 Summary, 300
10.6 Questions for Discussion, 301
11 Translational Clinical Trials 302
11.1 Introduction, 302
11.1.1 Therapeutic Intent or Not?, 303
11.1.2 Mechanistic Trials, 304
11.1.3 Marker Threshold Designs Are Strongly Biased, 305
11.2 Inferential Paradigms, 308
11.2.1 Biologic Paradigm, 308
11.2.2 Clinical Paradigm, 310
11.2.3 Surrogate Paradigm, 311
11.3 Evidence and Theory, 312
11.3.1 Biological Models Are a Key to Translational Trials, 313
11.4 Translational Trials Defined, 313
11.4.1 Translational Paradigm, 313
11.4.2 Character and Definition, 315
11.4.3 Small or "Pilot" Does Not Mean Translational, 316
11.4.4 Hypothetical Example, 316
11.4.5 Nesting Translational Studies, 317
11.5 Information From Translational Trials, 317
11.5.1 Surprise Can Be Defined Mathematically, 318
11.5.2 Parameter Uncertainty Versus Outcome Uncertainty, 318
11.5.3 Expected Surprise and Entropy, 319
11.5.4 Information/Entropy Calculated From Small Samples Is Biased, 321
11.5.5 Variance of Information/Entropy, 322
11.5.6 Sample Size for Translational Trials, 324
11.5.7 Validity, 327
11.6 Summary, 328
11.7 Questions for Discussion, 328
12 Early Development and Dose-Finding 329
12.1 Introduction, 329
12.2 Basic Concepts, 330
12.2.1 Therapeutic Intent, 330
12.2.2 Feasibility, 331
12.2.3 Dose versus Efficacy, 332
12.3 Essential Concepts for Dose versus Risk, 333
12.3.1 What Does the Terminology Mean?, 333
12.3.2 Distinguish Dose-Risk From Dose-Efficacy, 334
12.3.3 Dose Optimality Is a Design Definition, 335
12.3.4 Unavoidable Subjectivity, 335
12.3.5 Sample Size Is an Outcome of Dose-Finding Studies, 336
12.3.6 Idealized Dose-Finding Design, 336
12.4 Dose-Ranging, 338
12.4.1 Some Historical Designs, 338
12.4.2 Typical Dose-Ranging Design, 339
12.4.3 Operating Characteristics Can Be Calculated, 340
12.4.4 Modifications, Strengths, and Weaknesses, 343
12.5 Dose-Finding Is Model Based, 344
12.5.1 Mathematical Models Facilitate Inferences, 345
12.5.2 Continual Reassessment Method, 345
12.5.3 Pharmacokinetic Measurements Might Be Used to Improve CRM Dose Escalations, 349
12.5.4 The CRM Is an Attractive Design to Criticize, 350
12.5.5 CRM Clinical Examples, 350
12.5.6 Dose Distributions, 351
12.5.7 Estimation with Overdose Control (EWOC), 351
12.5.8 Randomization in Early Development?, 353
12.5.9 Phase I Data Have Other Uses, 353
12.6 General Dose-Finding Issues, 354
12.6.1 The General Dose-Finding Problem Is Unsolved, 354
12.6.2 More than One Drug, 356
12.6.3 More than One Outcome, 361
12.6.4 Envelope Simulation, 363
12.7 Summary, 366
12.8 Questions for Discussion, 368
13 Middle Development 370
13.1 Introduction, 370
13.1.1 Estimate Treatment Effects, 371
13.2 Characteristics of Middle Development, 372
13.2.1 Constraints, 373
13.2.2 Outcomes, 374
13.2.3 Focus, 375
13.3 Design Issues, 375
13.3.1 Choices in Middle Development, 375
13.3.2 When to Skip Middle Development, 376
13.3.3 Randomization, 377
13.3.4 Other Design Issues, 378
13.4 Middle Development Distills True Positives, 379
13.5 Futility and Nonsuperiority Designs, 381
13.5.1 Asymmetry in Error Control, 382
13.5.2 Should We Control False Positives or False Negatives?, 383
13.5.3 Futility Design Example, 384
13.5.4 A Conventional Approach to Futility, 385
13.6 Dose-Efficacy Questions, 385
13.7 Randomized Comparisons, 386
13.7.1 When to Perform an Error-Prone Comparative Trial, 387
13.7.2 Examples, 388
13.7.3 Randomized Selection, 389
13.8 Cohort Mixtures, 392
13.9 Summary, 395
13.10 Questions for Discussion, 396
14 Comparative Trials 397
14.1 Introduction, 397
14.2 Elements of Reliability, 398
14.2.1 Key Features, 399
14.2.2 Flexibilities, 400
14.2.3 Other Design Issues, 400
14.3 Biomarker-Based Comparative Designs, 402
14.3.1 Biomarkers Are Diverse, 402
14.3.2 Enrichment, 404
14.3.3 Biomarker-Stratified, 404
14.3.4 Biomarker-Strategy, 405
14.3.5 Multiple-Biomarker Signal-Finding, 406
14.3.6 Prospective-Retrospective Evaluation of a Biomarker, 407
14.3.7 Master Protocols, 407
14.4 Some Special Comparative Designs, 408
14.4.1 Randomized Discontinuation, 408
14.4.2 Delayed Start, 409
14.4.3 Cluster Randomization, 410
14.4.4 Non Inferiority, 410
14.4.5 Multiple Agents versus Control, 410
14.5 Summary, 411
14.6 Questions for Discussion, 412
15 Adaptive Design Features 413
15.1 Introduction, 413
15.1.1 Advantages and Disadvantages of AD, 414
15.1.2 Design Adaptations Are Tools, Not a Class, 416
15.1.3 Perspective on Bayesian Methods, 417
15.1.4 The Pipeline Is the Main Adaptive Tool, 417
15.2 Some Familiar Adaptations, 418
15.2.1 Dose-Finding Is Adaptive, 418
15.2.2 Adaptive Randomization, 418
15.2.3 Staging is Adaptive, 422
15.2.4 Dropping a Treatment Arm or Subset, 423
15.3 Biomarker Adaptive Trials, 423
15.4 Re-Designs, 425
15.4.1 Sample Size Re-Estimation Requires Caution, 425
15.5 Seamless Designs, 427
15.6 Barriers to the Use of AD, 428
15.7 Adaptive Design Case Study, 428
15.8 Summary, 429
15.9 Questions for Discussion, 429
16 Sample Size and Power 430
16.1 Introduction, 430
16.2 Principles, 431
16.2.1 What Is Precision?, 432
16.2.2 What Is Power?, 433
16.2.3 What Is Evidence?, 434
16.2.4 Sample Size and Power Calculations Are Approximations, 435
16.2.5 The Relationship between Power/Precision and Sample
Size Is Quadratic, 435
16.3 Early Developmental Trials, 436
16.3.1 Translational Trials, 436
16.3.2 Dose-Finding Trials, 437
16.4 Simple Estimation Designs, 438
16.4.1 Confidence Intervals for a Mean Provide a Sample Size Approach, 438
16.4.2 Estimating Proportions Accurately, 440
16.4.3 Exact Binomial Confidence Limits Are Helpful, 441
16.4.4 Precision Helps Detect Improvement, 444
16.4.5 Bayesian Binomial Confidence Intervals, 446
16.4.6 A Bayesian Approach Can Use Prior Information, 447
16.4.7 Likelihood-Based Approach for Proportions, 450
16.5 Event Rates, 451
16.5.1 Confidence Intervals for Event Rates Can Determine Sample Size, 451
16.5.2 Likelihood-Based Approach for Event Rates, 454
16.6 Staged Studies, 455
16.6.1 Ineffective or Unsafe Treatments Should Be Discarded Early, 455
16.6.2 Two-Stage Designs Increase Efficiency, 456
16.7 Comparative Trials, 457
16.7.1 How to Choose Type I and II Error Rates?, 459
16.7.2 Comparisons Using the t-Test Are a Good Learning Example, 459
16.7.3 Likelihood-Based Approach, 462
16.7.4 Dichotomous Responses Are More Complex, 463
16.7.5 Hazard Comparisons Yield Similar Equations, 464
16.7.6 Parametric and Nonparametric Equations Are Connected, 467
16.7.7 Accommodating Unbalanced Treatment Assignments, 467
16.7.8 A Simple Accrual Model Can Also Be Incorporated, 469
16.7.9 Stratification, 471
16.7.10 Noninferiority, 472
16.8 Expanded Safety Trials, 478
16.8.1 Model Rare Events with the Poisson Distribution, 479
16.8.2 Likelihood Approach for Poisson Rates, 479
16.9 Other Considerations, 481
16.9.1 Cluster Randomization Requires Increased Sample Size, 481
16.9.2 Simple Cost Optimization, 482
16.9.3 Increase the Sample Size for Nonadherence, 482
16.9.4 Simulated Lifetables Can Be a Simple Design Tool, 485
16.9.5 Sample Size for Prognostic Factor Studies, 486
16.9.6 Computer Programs Simplify Calculations, 487
16.9.7 Simulation Is a Powerful and Flexible Design Alternative, 487
16.9.8 Power Curves Are Sigmoid Shaped, 488
16.10 Summary, 489
16.11 Questions for Discussion, 490
17 Treatment Allocation 492
17.1 Introduction, 492
17.1.1 Balance and Bias Are Independent, 493
17.2 Randomization, 494
17.2.1 Heuristic Proof of the Value of Randomization, 495
17.2.2 Control the Influence of Unknown Factors, 497
17.2.3 Haphazard Assignments Are Not Random, 498
17.2.4 Simple Randomization Can Yield Imbalances, 499
17.3 Constrained Randomization, 500
17.3.1 Blocking Improves Balance, 500
17.3.2 Blocking and Stratifying Balances Prognostic Factors, 501
17.3.3 Other Considerations Regarding Blocking, 503
17.4 Adaptive Allocation, 504
17.4.1 Urn Designs Also Improve Balance, 504
17.4.2 Minimization Yields Tight Balance, 504
17.4.3 Play the Winner, 505
17.5 Other Issues Regarding Randomization, 507
17.5.1 Administration of the Randomization, 507
17.5.2 Computers Generate Pseudorandom Numbers, 508
17.5.3 Randomized Treatment Assignment Justifies Type I Errors, 509
17.6 Unequal Treatment Allocation, 514
17.6.1 Subsets May Be of Interest, 514
17.6.2 Treatments May Differ Greatly in Cost, 515
17.6.3 Variances May Be Different, 515
17.6.4 Multiarm Trials May Require Asymmetric Allocation, 516
17.6.5 Generalization, 517
17.6.6 Failed Randomization?, 518
17.7 Randomization Before Consent, 519
17.8 Summary, 520
17.9 Questions for Discussion, 520
18 Treatment Effects Monitoring 522
18.1 Introduction, 522
18.1.1 Motives for Monitoring, 523
18.1.2 Components of Responsible Monitoring, 524
18.1.3 Trials Can Be Stopped for a Variety of Reasons, 524
18.1.4 There Is Tension in the Decision to Stop, 526
18.2 Administrative Issues in Trial Monitoring, 527
18.2.1 Monitoring of Single-Center Studies Relies on Periodic Investigator Reporting, 527
18.2.2 Composition and Organization of the TEMC, 528
18.2.3 Complete Objectivity Is Not Ethical, 535
18.2.4 Independent Experts in Monitoring, 537
18.3 Organizational Issues Related to Monitoring, 537
18.3.1 Initial TEMC Meeting, 538
18.3.2 The TEMC Assesses Baseline Comparability, 538
18.3.3 The TEMC Reviews Accrual and Expected Time to Study Completion, 539
18.3.4 Timeliness of Data and Reporting Lags, 539
18.3.5 Data Quality Is a Major Focus of the TEMC, 540
18.3.6 The TEMC Reviews Safety and Toxicity Data, 541
18.3.7 Efficacy Differences Are Assessed by the TEMC, 541
18.3.8 The TEMC Should Address Some Practical Questions Specifically, 541
18.3.9 The TEMC Mechanism Has Potential Weaknesses, 544
18.4 Statistical Methods for Monitoring, 545
18.4.1 There Are Several Approaches to Evaluating Incomplete Evidence, 545
18.4.2 Monitoring Developmental Trials for Risk, 547
18.4.3 Likelihood-Based Methods, 551
18.4.4 Bayesian Methods, 557
18.4.5 Decision-Theoretic Methods, 559
18.4.6 Frequentist Methods, 560
18.4.7 Other Monitoring Tools, 566
18.4.8 Some Software, 570
18.5 Summary, 570
18.6 Questions for Discussion, 572
19 Counting Subjects and Events 573
19.1 Introduction, 573
19.2 Imperfection and Validity, 574
19.3 Treatment Nonadherence, 575
19.3.1 Intention to Treat Is a Policy of Inclusion, 575
19.3.2 Coronary Drug Project Results Illustrate the Pitfalls of Exclusions Based on Nonadherence, 576
19.3.3 Statistical Studies Support the ITT Approach, 577
19.3.4 Trials Are Tests of Treatment Policy, 577
19.3.5 ITT Analyses Cannot Always Be Applied, 578
19.3.6 Trial Inferences Depend on the Experiment Design, 579
19.4 Protocol Nonadherence, 580
19.4.1 Eligibility, 580
19.4.2 Treatment, 581
19.4.3 Defects in Retrospect, 582
19.5 Data Imperfections, 583
19.5.1 Evaluability Criteria Are a Methodologic Error, 583
19.5.2 Statistical Methods Can Cope with Some Types of Missing Data, 584
19.6 Summary, 588
19.7 Questions for Discussion, 589
20 Estimating Clinical Effects 590
20.1 Introduction, 590
20.1.1 Invisibility Works Against Validity, 591
20.1.2 Structure Aids Internal and External Validity, 591
20.1.3 Estimates of Risk Are Natural and Useful, 592
20.2 Dose-Finding and Pharmacokinetic Trials, 594
20.2.1 Pharmacokinetic Models Are Essential for Analyzing DF Trials, 594
20.2.2 A Two-Compartment Model Is Simple but Realistic, 595
20.2.3 PK Models Are Used By "Model Fitting", 598
20.3 Middle Development Studies, 599
20.3.1 Mesothelioma Clinical Trial Example, 599
20.3.2 Summarize Risk for Dichotomous Factors, 600
20.3.3 Nonparametric Estimates of Survival Are Robust, 601
20.3.4 Parametric (Exponential) Summaries of Survival Are Efficient, 603
20.3.5 Percent Change and Waterfall Plots, 605
20.4 Randomized Comparative Trials, 606
20.4.1 Examples of Comparative Trials Used in This Section, 607
20.4.2 Continuous Measures Estimate Treatment Differences, 608
20.4.3 Baseline Measurements Can Increase Precision, 609
20.4.4 Comparing Counts, 610
20.4.5 Nonparametric Survival Comparisons, 612
20.4.6 Risk (Hazard) Ratios and Confidence Intervals Are Clinically Useful Data Summaries, 614
20.4.7 Statistical Models Are Necessary Tools, 615
20.5 Problems With P-Values, 616
20.5.1 P-Values Do Not Represent Treatment Effects, 618
20.5.2 P-Values Do Not Imply Reproducibility, 618
20.5.3 P-Values Do Not Measure Evidence, 619
20.6 Strength of Evidence Through Support Intervals, 620
20.6.1 Support Intervals Are Based on the Likelihood Function, 620
20.6.2 Support Intervals Can Be Used with Any Outcome, 621
20.7 Special Methods of Analysis, 622
20.7.1 The Bootstrap Is Based on Resampling, 623
20.7.2 Some Clinical Questions Require Other Special Methods of Analysis, 623
20.8 Exploratory Analyses, 628
20.8.1 Clinical Trial Data Lend Themselves to Exploratory Analyses, 628
20.8.2 Multiple Tests Multiply Type I Errors, 629
20.8.3 Kinds of Multiplicity, 630
20.8.4 Inevitible Risks from Subgroups, 630
20.8.5 Tale of a Subset Analysis Gone Wrong, 632
20.8.6 Perspective on Subgroup Analyses, 635
20.8.7 Effects the Trial Was Not Designed to Detect, 636
20.8.8 Safety Signals, 637
20.8.9 Subsets, 637
20.8.10 Interactions, 638
20.9 Summary, 639
20.10 Questions for Discussion, 640
21 Prognostic Factor Analyses 644
21.1 Introduction, 644
21.1.1 Studying Prognostic Factors is Broadly Useful, 645
21.1.2 Prognostic Factors Can Be Constant or Time-Varying, 646
21.2 Model-Based Methods, 647
21.2.1 Models Combine Theory and Data, 647
21.2.2 Scale and Coding May Be Important, 648
21.2.3 Use Flexible Covariate Models, 648
21.2.4 Building Parsimonious Models Is the Next Step, 650
21.2.5 Incompletely Specified Models May Yield Biased Estimates, 655
21.2.6 Study Second-Order Effects (Interactions), 656
21.2.7 PFAs Can Help Describe Risk Groups, 656
21.2.8 Power and Sample Size for PFAs, 660
21.3 Adjusted Analyses of Comparative Trials, 661
21.3.1 What Should We Adjust For?, 662
21.3.2 What Can Happen?, 663
21.3.3 Brain Tumor Case Study, 664
21.4 PFAS Without Models, 666
21.4.1 Recursive Partitioning Uses Dichotomies, 666
21.4.2 Neural Networks Are Used for Pattern Recognition, 667
21.5 Summary, 669
21.6 Questions for Discussion, 669
22 Factorial Designs 671
22.1 Introduction, 671
22.2 Characteristics of Factorial Designs, 672
22.2.1 Interactions or Efficiency, But Not Both Simultaneously, 672
22.2.2 Factorial Designs Are Defined by Their Structure, 672
22.2.3 Factorial Designs Can Be Made Efficient, 674
22.3 Treatment Interactions, 675
22.3.1 Factorial Designs Are the Only Way to Study Interactions, 675
22.3.2 Interactions Depend on the Scale of Measurement, 677
22.3.3 The Interpretation of Main Effects Depends on Interactions, 677
22.3.4 Analyses Can Employ Linear Models, 678
22.4 Examples of Factorial Designs, 680
22.5 Partial, Fractional, and Incomplete Factorials, 682
22.5.1 Use Partial Factorial Designs When Interactions Are Absent, 682
22.5.2 Incomplete Designs Present Special Problems, 682
22.6 Summary, 683
22.7 Questions for Discussion, 683
23 Crossover Designs 684
23.1 Introduction, 684
23.1.1 Other Ways of Giving Multiple Treatments Are Not Crossovers, 685
23.1.2 Treatment Periods May Be Randomly Assigned, 686
23.2 Advantages and Disadvantages, 686
23.2.1 Crossover Designs Can Increase Precision, 687
23.2.2 A Crossover Design Might Improve Recruitment, 687
23.2.3 Carryover Effects Are a Potential Problem, 688
23.2.4 Dropouts Have Strong Effects, 689
23.2.5 Analysis is More Complex Than for a Parallel-Group Design, 689
23.2.6 Prerequisites Are Needed to Apply Crossover Designs, 689
23.2.7 Other Uses for the Design, 690
23.3 Analysis, 691
23.3.1 Simple Approaches, 691
23.3.2 Analysis Can Be Based on a Cell Means Model, 692
23.3.3 Other Issues in Analysis, 696
23.4 Classic Case Study, 696
23.5 Summary, 696
23.6 Questions for Discussion, 697
24 Meta-Analyses 698
24.1 Introduction, 698
24.1.1 Meta-Analyses Formalize Synthesis and Increase Precision, 699
24.2 A Sketch of Meta-Analysis Methods, 700
24.2.1 Meta-Analysis Necessitates Prerequisites, 700
24.2.2 Many Studies Are Potentially Relevant, 701
24.2.3 Select Studies, 702
24.2.4 Plan the Statistical Analysis, 703
24.2.5 Summarize the Data Using Observed and Expected, 703
24.3 Other Issues, 705
24.3.1 Cumulative Meta-Analyses, 705
24.3.2 Meta-Analyses Have Practical and Theoretical Limitations, 706
24.3.3 Meta-Analysis Has Taught Useful Lessons, 707
24.4 Summary, 707
24.5 Questions for Discussion, 708
25 Reporting and Authorship 709
25.1 Introduction, 709
25.2 General Issues in Reporting, 710
25.2.1 Uniformity Improves Comprehension, 711
25.2.2 Quality of the Literature, 712
25.2.3 Peer Review Is the Only Game in Town, 712
25.2.4 Publication Bias Can Distort Impressions Based on the Literature, 713
25.3 Clinical Trial Reports, 715
25.3.1 General Considerations, 716
25.3.2 Employ a Complete Outline for Comparative Trial Reporting, 721
25.4 Authorship, 726
25.4.1 Inclusion and Ordering, 727
25.4.2 Responsibility of Authorship, 727
25.4.3 Authorship Models, 728
25.4.4 Some Other Practicalities, 730
25.5 Other Issues in Disseminating Results, 731
25.5.1 Open Access, 731
25.5.2 Clinical Alerts, 731
25.5.3 Retractions, 732
25.6 Summary, 732
25.7 Questions for Discussion, 733
26 Misconduct and Fraud in Clinical Research 734
26.1 Introduction, 734
26.1.1 Integrity and Accountability Are Critically Important, 736
26.1.2 Fraud and Misconduct Are Difficult to Define, 738
26.2 Research Practices, 741
26.2.1 Misconduct May Be Increasing in Frequency, 741
26.2.2 Causes of Misconduct, 742
26.3 Approach to Allegations of Misconduct, 743
26.3.1 Institutions, 744
26.3.2 Problem Areas, 746
26.4 Characteristics of Some Misconduct Cases, 747
26.4.1 Darsee Case, 747
26.4.2 Poisson (NSABP) Case, 749
26.4.3 Two Recent Cases from Germany, 752
26.4.4 Fiddes Case, 753
26.4.5 Potti Case, 754
26.5 Lessons, 754
26.5.1 Recognizing
About the Companion Website xxviii
1 Preliminaries 1
1.1 Introduction, 1
1.2 Audiences, 2
1.3 Scope, 3
1.4 Other Sources of Knowledge, 5
1.5 Notation and Terminology, 6
1.5.1 Clinical Trial Terminology, 7
1.5.2 Drug Development Traditionally Recognizes Four Trial Design Types, 7
1.5.3 Descriptive Terminology Is Better, 8
1.6 Examples, Data, and Programs, 9
1.7 Summary, 9
2 Clinical Trials as Research 10
2.1 Introduction, 10
2.2 Research, 13
2.2.1 What Is Research?, 13
2.2.2 Clinical Reasoning Is Based on the Case History, 14
2.2.3 Statistical Reasoning Emphasizes Inference Based on Designed Data Production, 16
2.2.4 Clinical and Statistical Reasoning Converge in Research, 17
2.3 Defining Clinical Trials, 19
2.3.1 Mixing of Clinical and Statistical Reasoning Is Recent, 19
2.3.2 Clinical Trials Are Rigorously Defined, 21
2.3.3 Theory and Data, 22
2.3.4 Experiments Can Be Misunderstood, 23
2.3.5 Clinical Trials and the Frankenstein Myth, 25
2.3.6 Cavia porcellus, 26
2.3.7 Clinical Trials as Science, 26
2.3.8 Trials and Statistical Methods Fit within a Spectrum of Clinical Research, 28
2.4 Practicalities of Usage, 29
2.4.1 Predicates for a Trial, 29
2.4.2 Trials Can Provide Confirmatory Evidence, 29
2.4.3 Clinical Trials Are Reliable Albeit Unwieldy and Messy, 30
2.4.4 Trials Are Difficult to Apply in Some Circumstances, 31
2.4.5 Randomized Studies Can Be Initiated Early, 32
2.4.6 What Can I learn from = 20?, 33
2.5 Nonexperimental Designs, 35
2.5.1 Other Methods Are Valid forMaking Some Clinical Inferences, 35
2.5.2 Some Specific Nonexperimental Designs, 38
2.5.3 Causal Relationships, 40
2.5.4 Will Genetic Determinism Replace Design?, 41
2.6 Summary, 41
2.7 Questions for Discussion, 41
3 Why Clinical Trials Are Ethical 43
3.1 Introduction, 43
3.1.1 Science and Ethics Share Objectives, 44
3.1.2 Equipoise and Uncertainty, 46
3.2 Duality, 47
3.2.1 Clinical Trials Sharpen, But Do Not Create, Duality, 47
3.2.2 A Gene Therapy Tragedy Illustrates Duality, 48
3.2.3 Research and Practice Are Convergent, 48
3.2.4 Hippocratic Tradition Does Not Proscribe Clinical Trials, 52
3.2.5 Physicians Always Have Multiple Roles, 54
3.3 Historically Derived Principles of Ethics, 57
3.3.1 Nuremberg Contributed an Awareness of the Worst Problems, 57
3.3.2 High-Profile Mistakes Were Made in the United States, 58
3.3.3 The Helsinki Declaration Was Widely Adopted, 58
3.3.4 Other International Guidelines Have Been Proposed, 61
3.3.5 Institutional Review Boards Provide Ethics Oversight, 62
3.3.6 Ethics Principles Relevant to Clinical Trials, 63
3.4 Contemporary Foundational Principles, 65
3.4.1 Collaborative Partnership, 66
3.4.2 Scientific Value, 66
3.4.3 Scientific Validity, 66
3.4.4 Fair Subject Selection, 67
3.4.5 Favorable Risk-Benefit, 67
3.4.6 Independent Review, 68
3.4.7 Informed Consent, 68
3.4.8 Respect for Subjects, 71
3.5 Methodologic Reflections, 72
3.5.1 Practice Based on Unproven Treatments Is Not Ethical, 72
3.5.2 Ethics Considerations Are Important Determinants of Design, 74
3.5.3 Specific Methods Have Justification, 75
3.6 Professional Conduct, 79
3.6.1 Advocacy, 79
3.6.2 Physician to Physician Communication Is Not Research, 81
3.6.3 Investigator Responsibilities, 82
3.6.4 Professional Ethics, 83
3.7 Summary, 85
3.8 Questions for Discussion, 86
4 Contexts for Clinical Trials 87
4.1 Introduction, 87
4.1.1 Clinical Trial Registries, 88
4.1.2 Public Perception Versus Science, 90
4.2 Drugs, 91
4.2.1 Are Drugs Special?, 92
4.2.2 Why Trials Are Used Extensively for Drugs, 93
4.3 Devices, 95
4.3.1 Use of Trials for Medical Devices, 95
4.3.2 Are Devices Different from Drugs?, 97
4.3.3 Case Study, 98
4.4 Prevention, 99
4.4.1 The Prevention versus Therapy Dichotomy Is Over-worked, 100
4.4.2 Vaccines and Biologicals, 101
4.4.3 Ebola 2014 and Beyond, 102
4.4.4 A Perspective on Risk-Benefit, 103
4.4.5 Methodology and Framework for Prevention Trials, 105
4.5 Complementary and Alternative Medicine, 106
4.5.1 Science Is the Study of Natural Phenomena, 108
4.5.2 Ignorance Is Important, 109
4.5.3 The Essential Paradox of CAM and Clinical Trials, 110
4.5.4 Why Trials Have Not Been Used Extensively in CAM, 111
4.5.5 Some Principles for Rigorous Evaluation, 113
4.5.6 Historic Examples, 115
4.6 Surgery and Skill-Dependent Therapies, 116
4.6.1 Why Trials Have Been Used Less Extensively in Surgery, 118
4.6.2 Reasons Why Some Surgical Therapies Require Less Rigorous Study Designs, 120
4.6.3 Sources of Variation, 121
4.6.4 Difficulties of Inference, 121
4.6.5 Control of Observer Bias Is Possible, 122
4.6.6 Illustrations from an Emphysema Surgery Trial, 124
4.7 A Brief View of Some Other Contexts, 130
4.7.1 Screening Trials, 130
4.7.2 Diagnostic Trials, 134
4.7.3 Radiation Therapy, 134
4.8 Summary, 135
4.9 Questions for Discussion, 136
5 Measurement 137
5.1 Introduction, 137
5.1.1 Types of Uncertainty, 138
5.2 Objectives, 140
5.2.1 Estimation Is The Most Common Objective, 141
5.2.2 Selection Can Also Be an Objective, 141
5.2.3 Objectives Require Various Scales of Measurement, 142
5.3 Measurement Design, 143
5.3.1 Mixed Outcomes and Predictors, 143
5.3.2 Criteria for Evaluating Outcomes, 144
5.3.3 Prefer Hard or Objective Outcomes, 145
5.3.4 Outcomes Can Be Quantitative or Qualitative, 146
5.3.5 Measures Are Useful and Efficient Outcomes, 146
5.3.6 Some Outcomes Are Summarized as Counts, 147
5.3.7 Ordered Categories Are Commonly Used for Severity or Toxicity, 147
5.3.8 Unordered Categories Are Sometimes Used, 148
5.3.9 Dichotomies Are Simple Summaries, 148
5.3.10 Measures of Risk, 149
5.3.11 Primary and Others, 153
5.3.12 Composites, 154
5.3.13 Event Times and Censoring, 155
5.3.14 Longitudinal Measures, 160
5.3.15 Central Review, 161
5.3.16 Patient Reported Outcomes, 161
5.4 Surrogate Outcomes, 162
5.4.1 Surrogate Outcomes Are Disease-Specific, 164
5.4.2 Surrogate Outcomes Can Make Trials More Efficient, 167
5.4.3 Surrogate Outcomes Have Significant Limitations, 168
5.5 Summary, 170
5.6 Questions for Discussion, 171
6 Random Error and Bias 172
6.1 Introduction, 172
6.1.1 The Effects of Random and Systematic Errors Are Distinct, 173
6.1.2 Hypothesis Tests versus Significance Tests, 174
6.1.3 Hypothesis Tests Are Subject to Two Types of Random Error, 175
6.1.4 Type I Errors Are Relatively Easy to Control, 176
6.1.5 The Properties of Confidence IntervalsAre Similar toHypothesis Tests, 176
6.1.6 Using a one- or two-sided hypothesis test is not the right question, 177
6.1.7 P-Values Quantify the Type I Error, 178
6.1.8 Type II Errors Depend on the Clinical Difference of Interest, 178
6.1.9 Post Hoc Power Calculations Are Useless, 180
6.2 Clinical Bias, 181
6.2.1 Relative Size of Random Error and Bias is Important, 182
6.2.2 Bias Arises from Numerous Sources, 182
6.2.3 Controlling Structural Bias is Conceptually Simple, 185
6.3 Statistical Bias, 188
6.3.1 Selection Bias, 188
6.3.2 Some Statistical Bias Can Be Corrected, 192
6.3.3 Unbiasedness is Not the Only Desirable Attribute of an Estimator, 192
6.4 Summary, 194
6.5 Questions for Discussion, 194
7 Statistical Perspectives 196
7.1 Introduction, 196
7.2 Differences in Statistical Perspectives, 197
7.2.1 Models and Parameters, 197
7.2.2 Philosophy of Inference Divides Statisticians, 198
7.2.3 Resolution, 199
7.2.4 Points of Agreement, 199
7.3 Frequentist, 202
7.3.1 Binomial Case Study, 203
7.3.2 Other Issues, 204
7.4 Bayesian, 204
7.4.1 Choice of a Prior Distribution Is a Source of Contention, 205
7.4.2 Binomial Case Study, 206
7.4.3 Bayesian Inference Is Different, 209
7.5 Likelihood, 210
7.5.1 Binomial Case Study, 211
7.5.2 Likelihood-Based Design, 211
7.6 Statistics Issues, 212
7.6.1 Perspective, 212
7.6.2 Statistical Procedures Are Not Standardized, 213
7.6.3 Practical Controversies Related to Statistics Exist, 214
7.7 Summary, 215
7.8 Questions for Discussion, 216
8 Experiment Design in Clinical Trials 217
8.1 Introduction, 217
8.2 Trials As Simple Experiment Designs, 218
8.2.1 Design Space Is Chaotic, 219
8.2.2 Design Is Critical for Inference, 220
8.2.3 The Question Drives the Design, 220
8.2.4 Design Depends on the Observation Model As Well As the
Biological Question, 221
8.2.5 Comparing Designs, 222
8.3 Goals of Experiment Design, 223
8.3.1 Control of Random Error and Bias Is the Goal, 223
8.3.2 Conceptual Simplicity Is Also a Goal, 223
8.3.3 Encapsulation of Subjectivity, 224
8.3.4 Leech Case Study, 225
8.4 Design Concepts, 225
8.4.1 The Foundations of Design Are Observation and Theory, 226
8.4.2 A Lesson from the Women's Health Initiative, 227
8.4.3 Experiments Use Three Components of Design, 229
8.5 Design Features, 230
8.5.1 Enrichment, 231
8.5.2 Replication, 232
8.5.3 Experimental and Observational Units, 232
8.5.4 Treatments and Factors, 233
8.5.5 Nesting, 233
8.5.6 Randomization, 234
8.5.7 Blocking, 234
8.5.8 Stratification, 235
8.5.9 Masking, 236
8.6 Special Design Issues, 237
8.6.1 Placebos, 237
8.6.2 Equivalence and Noninferiority, 240
8.6.3 Randomized Discontinuation, 241
8.6.4 Hybrid Designs May Be Needed for Resolving Special Questions, 242
8.6.5 Clinical Trials Cannot Meet Certain Objectives, 242
8.7 Importance of the Protocol Document, 244
8.7.1 Protocols Have Many Functions, 244
8.7.2 Deviations from Protocol Specifications are Common, 245
8.7.3 Protocols Are Structured, Logical, and Complete, 246
8.8 Summary, 252
8.9 Questions for Discussion, 253
9 The Trial Cohort 254
9.1 Introduction, 254
9.2 Cohort Definition and Selection, 255
9.2.1 Eligibility and Exclusions, 255
9.2.2 Active Sampling and Enrichment, 257
9.2.3 Participation may select subjects with better prognosis, 258
9.2.4 Quantitative Selection Criteria Versus False Precision, 262
9.2.5 Comparative Trials Are Not Sensitive to Selection, 263
9.3 Modeling Accrual, 264
9.3.1 Using a Run-In Period, 264
9.3.2 Estimate Accrual Quantitatively, 265
9.4 Inclusiveness, Representation, and Interactions, 267
9.4.1 Inclusiveness Is a Worthy Goal, 267
9.4.2 Barriers Can Hinder Trial Participation, 268
9.4.3 Efficacy versus Effectiveness Trials, 269
9.4.4 Representation: Politics Blunders into Science, 270
9.5 Summary, 275
9.6 Questions for Discussion, 275
10 Development Paradigms 277
10.1 Introduction, 277
10.1.1 Stages of Development, 278
10.1.2 Trial Design versus Development Design, 280
10.1.3 Companion Diagnostics in Cancer, 281
10.2 Pipeline Principles and Problems, 281
10.2.1 The Paradigm Is Not Linear, 282
10.2.2 Staging Allows Efficiency, 282
10.2.3 The Pipeline Impacts Study Design, 283
10.2.4 Specificity and Pressures Shape the Pipeline, 283
10.2.5 Problems with Trials, 284
10.2.6 Problems in the Pipeline, 286
10.3 A Simple Quantitative Pipeline, 286
10.3.1 Pipeline Operating Characteristics Can Be Derived, 286
10.3.2 Implications May Be Counterintuitive, 288
10.3.3 Optimization Yields Insights, 288
10.3.4 Overall Implications for the Pipeline, 291
10.4 Late Failures, 292
10.4.1 Generic Mistakes in Evaluating Evidence, 293
10.4.2 "Safety" Begets Efficacy Testing, 293
10.4.3 Pressure to Advance Ideas Is Unprecedented, 294
10.4.4 Scientists Believe Weird Things, 294
10.4.5 Confirmation Bias, 295
10.4.6 Many Biological Endpoints Are Neither Predictive nor Prognostic, 296
10.4.7 Disbelief Is Easier to Suspend Than Belief, 296
10.4.8 Publication Bias, 297
10.4.9 Intellectual Conflicts of Interest, 297
10.4.10 Many Preclinical Models Are Invalid, 298
10.4.11 Variation Despite Genomic Determinism, 299
10.4.12 Weak Evidence Is Likely to Mislead, 300
10.5 Summary, 300
10.6 Questions for Discussion, 301
11 Translational Clinical Trials 302
11.1 Introduction, 302
11.1.1 Therapeutic Intent or Not?, 303
11.1.2 Mechanistic Trials, 304
11.1.3 Marker Threshold Designs Are Strongly Biased, 305
11.2 Inferential Paradigms, 308
11.2.1 Biologic Paradigm, 308
11.2.2 Clinical Paradigm, 310
11.2.3 Surrogate Paradigm, 311
11.3 Evidence and Theory, 312
11.3.1 Biological Models Are a Key to Translational Trials, 313
11.4 Translational Trials Defined, 313
11.4.1 Translational Paradigm, 313
11.4.2 Character and Definition, 315
11.4.3 Small or "Pilot" Does Not Mean Translational, 316
11.4.4 Hypothetical Example, 316
11.4.5 Nesting Translational Studies, 317
11.5 Information From Translational Trials, 317
11.5.1 Surprise Can Be Defined Mathematically, 318
11.5.2 Parameter Uncertainty Versus Outcome Uncertainty, 318
11.5.3 Expected Surprise and Entropy, 319
11.5.4 Information/Entropy Calculated From Small Samples Is Biased, 321
11.5.5 Variance of Information/Entropy, 322
11.5.6 Sample Size for Translational Trials, 324
11.5.7 Validity, 327
11.6 Summary, 328
11.7 Questions for Discussion, 328
12 Early Development and Dose-Finding 329
12.1 Introduction, 329
12.2 Basic Concepts, 330
12.2.1 Therapeutic Intent, 330
12.2.2 Feasibility, 331
12.2.3 Dose versus Efficacy, 332
12.3 Essential Concepts for Dose versus Risk, 333
12.3.1 What Does the Terminology Mean?, 333
12.3.2 Distinguish Dose-Risk From Dose-Efficacy, 334
12.3.3 Dose Optimality Is a Design Definition, 335
12.3.4 Unavoidable Subjectivity, 335
12.3.5 Sample Size Is an Outcome of Dose-Finding Studies, 336
12.3.6 Idealized Dose-Finding Design, 336
12.4 Dose-Ranging, 338
12.4.1 Some Historical Designs, 338
12.4.2 Typical Dose-Ranging Design, 339
12.4.3 Operating Characteristics Can Be Calculated, 340
12.4.4 Modifications, Strengths, and Weaknesses, 343
12.5 Dose-Finding Is Model Based, 344
12.5.1 Mathematical Models Facilitate Inferences, 345
12.5.2 Continual Reassessment Method, 345
12.5.3 Pharmacokinetic Measurements Might Be Used to Improve CRM Dose Escalations, 349
12.5.4 The CRM Is an Attractive Design to Criticize, 350
12.5.5 CRM Clinical Examples, 350
12.5.6 Dose Distributions, 351
12.5.7 Estimation with Overdose Control (EWOC), 351
12.5.8 Randomization in Early Development?, 353
12.5.9 Phase I Data Have Other Uses, 353
12.6 General Dose-Finding Issues, 354
12.6.1 The General Dose-Finding Problem Is Unsolved, 354
12.6.2 More than One Drug, 356
12.6.3 More than One Outcome, 361
12.6.4 Envelope Simulation, 363
12.7 Summary, 366
12.8 Questions for Discussion, 368
13 Middle Development 370
13.1 Introduction, 370
13.1.1 Estimate Treatment Effects, 371
13.2 Characteristics of Middle Development, 372
13.2.1 Constraints, 373
13.2.2 Outcomes, 374
13.2.3 Focus, 375
13.3 Design Issues, 375
13.3.1 Choices in Middle Development, 375
13.3.2 When to Skip Middle Development, 376
13.3.3 Randomization, 377
13.3.4 Other Design Issues, 378
13.4 Middle Development Distills True Positives, 379
13.5 Futility and Nonsuperiority Designs, 381
13.5.1 Asymmetry in Error Control, 382
13.5.2 Should We Control False Positives or False Negatives?, 383
13.5.3 Futility Design Example, 384
13.5.4 A Conventional Approach to Futility, 385
13.6 Dose-Efficacy Questions, 385
13.7 Randomized Comparisons, 386
13.7.1 When to Perform an Error-Prone Comparative Trial, 387
13.7.2 Examples, 388
13.7.3 Randomized Selection, 389
13.8 Cohort Mixtures, 392
13.9 Summary, 395
13.10 Questions for Discussion, 396
14 Comparative Trials 397
14.1 Introduction, 397
14.2 Elements of Reliability, 398
14.2.1 Key Features, 399
14.2.2 Flexibilities, 400
14.2.3 Other Design Issues, 400
14.3 Biomarker-Based Comparative Designs, 402
14.3.1 Biomarkers Are Diverse, 402
14.3.2 Enrichment, 404
14.3.3 Biomarker-Stratified, 404
14.3.4 Biomarker-Strategy, 405
14.3.5 Multiple-Biomarker Signal-Finding, 406
14.3.6 Prospective-Retrospective Evaluation of a Biomarker, 407
14.3.7 Master Protocols, 407
14.4 Some Special Comparative Designs, 408
14.4.1 Randomized Discontinuation, 408
14.4.2 Delayed Start, 409
14.4.3 Cluster Randomization, 410
14.4.4 Non Inferiority, 410
14.4.5 Multiple Agents versus Control, 410
14.5 Summary, 411
14.6 Questions for Discussion, 412
15 Adaptive Design Features 413
15.1 Introduction, 413
15.1.1 Advantages and Disadvantages of AD, 414
15.1.2 Design Adaptations Are Tools, Not a Class, 416
15.1.3 Perspective on Bayesian Methods, 417
15.1.4 The Pipeline Is the Main Adaptive Tool, 417
15.2 Some Familiar Adaptations, 418
15.2.1 Dose-Finding Is Adaptive, 418
15.2.2 Adaptive Randomization, 418
15.2.3 Staging is Adaptive, 422
15.2.4 Dropping a Treatment Arm or Subset, 423
15.3 Biomarker Adaptive Trials, 423
15.4 Re-Designs, 425
15.4.1 Sample Size Re-Estimation Requires Caution, 425
15.5 Seamless Designs, 427
15.6 Barriers to the Use of AD, 428
15.7 Adaptive Design Case Study, 428
15.8 Summary, 429
15.9 Questions for Discussion, 429
16 Sample Size and Power 430
16.1 Introduction, 430
16.2 Principles, 431
16.2.1 What Is Precision?, 432
16.2.2 What Is Power?, 433
16.2.3 What Is Evidence?, 434
16.2.4 Sample Size and Power Calculations Are Approximations, 435
16.2.5 The Relationship between Power/Precision and Sample
Size Is Quadratic, 435
16.3 Early Developmental Trials, 436
16.3.1 Translational Trials, 436
16.3.2 Dose-Finding Trials, 437
16.4 Simple Estimation Designs, 438
16.4.1 Confidence Intervals for a Mean Provide a Sample Size Approach, 438
16.4.2 Estimating Proportions Accurately, 440
16.4.3 Exact Binomial Confidence Limits Are Helpful, 441
16.4.4 Precision Helps Detect Improvement, 444
16.4.5 Bayesian Binomial Confidence Intervals, 446
16.4.6 A Bayesian Approach Can Use Prior Information, 447
16.4.7 Likelihood-Based Approach for Proportions, 450
16.5 Event Rates, 451
16.5.1 Confidence Intervals for Event Rates Can Determine Sample Size, 451
16.5.2 Likelihood-Based Approach for Event Rates, 454
16.6 Staged Studies, 455
16.6.1 Ineffective or Unsafe Treatments Should Be Discarded Early, 455
16.6.2 Two-Stage Designs Increase Efficiency, 456
16.7 Comparative Trials, 457
16.7.1 How to Choose Type I and II Error Rates?, 459
16.7.2 Comparisons Using the t-Test Are a Good Learning Example, 459
16.7.3 Likelihood-Based Approach, 462
16.7.4 Dichotomous Responses Are More Complex, 463
16.7.5 Hazard Comparisons Yield Similar Equations, 464
16.7.6 Parametric and Nonparametric Equations Are Connected, 467
16.7.7 Accommodating Unbalanced Treatment Assignments, 467
16.7.8 A Simple Accrual Model Can Also Be Incorporated, 469
16.7.9 Stratification, 471
16.7.10 Noninferiority, 472
16.8 Expanded Safety Trials, 478
16.8.1 Model Rare Events with the Poisson Distribution, 479
16.8.2 Likelihood Approach for Poisson Rates, 479
16.9 Other Considerations, 481
16.9.1 Cluster Randomization Requires Increased Sample Size, 481
16.9.2 Simple Cost Optimization, 482
16.9.3 Increase the Sample Size for Nonadherence, 482
16.9.4 Simulated Lifetables Can Be a Simple Design Tool, 485
16.9.5 Sample Size for Prognostic Factor Studies, 486
16.9.6 Computer Programs Simplify Calculations, 487
16.9.7 Simulation Is a Powerful and Flexible Design Alternative, 487
16.9.8 Power Curves Are Sigmoid Shaped, 488
16.10 Summary, 489
16.11 Questions for Discussion, 490
17 Treatment Allocation 492
17.1 Introduction, 492
17.1.1 Balance and Bias Are Independent, 493
17.2 Randomization, 494
17.2.1 Heuristic Proof of the Value of Randomization, 495
17.2.2 Control the Influence of Unknown Factors, 497
17.2.3 Haphazard Assignments Are Not Random, 498
17.2.4 Simple Randomization Can Yield Imbalances, 499
17.3 Constrained Randomization, 500
17.3.1 Blocking Improves Balance, 500
17.3.2 Blocking and Stratifying Balances Prognostic Factors, 501
17.3.3 Other Considerations Regarding Blocking, 503
17.4 Adaptive Allocation, 504
17.4.1 Urn Designs Also Improve Balance, 504
17.4.2 Minimization Yields Tight Balance, 504
17.4.3 Play the Winner, 505
17.5 Other Issues Regarding Randomization, 507
17.5.1 Administration of the Randomization, 507
17.5.2 Computers Generate Pseudorandom Numbers, 508
17.5.3 Randomized Treatment Assignment Justifies Type I Errors, 509
17.6 Unequal Treatment Allocation, 514
17.6.1 Subsets May Be of Interest, 514
17.6.2 Treatments May Differ Greatly in Cost, 515
17.6.3 Variances May Be Different, 515
17.6.4 Multiarm Trials May Require Asymmetric Allocation, 516
17.6.5 Generalization, 517
17.6.6 Failed Randomization?, 518
17.7 Randomization Before Consent, 519
17.8 Summary, 520
17.9 Questions for Discussion, 520
18 Treatment Effects Monitoring 522
18.1 Introduction, 522
18.1.1 Motives for Monitoring, 523
18.1.2 Components of Responsible Monitoring, 524
18.1.3 Trials Can Be Stopped for a Variety of Reasons, 524
18.1.4 There Is Tension in the Decision to Stop, 526
18.2 Administrative Issues in Trial Monitoring, 527
18.2.1 Monitoring of Single-Center Studies Relies on Periodic Investigator Reporting, 527
18.2.2 Composition and Organization of the TEMC, 528
18.2.3 Complete Objectivity Is Not Ethical, 535
18.2.4 Independent Experts in Monitoring, 537
18.3 Organizational Issues Related to Monitoring, 537
18.3.1 Initial TEMC Meeting, 538
18.3.2 The TEMC Assesses Baseline Comparability, 538
18.3.3 The TEMC Reviews Accrual and Expected Time to Study Completion, 539
18.3.4 Timeliness of Data and Reporting Lags, 539
18.3.5 Data Quality Is a Major Focus of the TEMC, 540
18.3.6 The TEMC Reviews Safety and Toxicity Data, 541
18.3.7 Efficacy Differences Are Assessed by the TEMC, 541
18.3.8 The TEMC Should Address Some Practical Questions Specifically, 541
18.3.9 The TEMC Mechanism Has Potential Weaknesses, 544
18.4 Statistical Methods for Monitoring, 545
18.4.1 There Are Several Approaches to Evaluating Incomplete Evidence, 545
18.4.2 Monitoring Developmental Trials for Risk, 547
18.4.3 Likelihood-Based Methods, 551
18.4.4 Bayesian Methods, 557
18.4.5 Decision-Theoretic Methods, 559
18.4.6 Frequentist Methods, 560
18.4.7 Other Monitoring Tools, 566
18.4.8 Some Software, 570
18.5 Summary, 570
18.6 Questions for Discussion, 572
19 Counting Subjects and Events 573
19.1 Introduction, 573
19.2 Imperfection and Validity, 574
19.3 Treatment Nonadherence, 575
19.3.1 Intention to Treat Is a Policy of Inclusion, 575
19.3.2 Coronary Drug Project Results Illustrate the Pitfalls of Exclusions Based on Nonadherence, 576
19.3.3 Statistical Studies Support the ITT Approach, 577
19.3.4 Trials Are Tests of Treatment Policy, 577
19.3.5 ITT Analyses Cannot Always Be Applied, 578
19.3.6 Trial Inferences Depend on the Experiment Design, 579
19.4 Protocol Nonadherence, 580
19.4.1 Eligibility, 580
19.4.2 Treatment, 581
19.4.3 Defects in Retrospect, 582
19.5 Data Imperfections, 583
19.5.1 Evaluability Criteria Are a Methodologic Error, 583
19.5.2 Statistical Methods Can Cope with Some Types of Missing Data, 584
19.6 Summary, 588
19.7 Questions for Discussion, 589
20 Estimating Clinical Effects 590
20.1 Introduction, 590
20.1.1 Invisibility Works Against Validity, 591
20.1.2 Structure Aids Internal and External Validity, 591
20.1.3 Estimates of Risk Are Natural and Useful, 592
20.2 Dose-Finding and Pharmacokinetic Trials, 594
20.2.1 Pharmacokinetic Models Are Essential for Analyzing DF Trials, 594
20.2.2 A Two-Compartment Model Is Simple but Realistic, 595
20.2.3 PK Models Are Used By "Model Fitting", 598
20.3 Middle Development Studies, 599
20.3.1 Mesothelioma Clinical Trial Example, 599
20.3.2 Summarize Risk for Dichotomous Factors, 600
20.3.3 Nonparametric Estimates of Survival Are Robust, 601
20.3.4 Parametric (Exponential) Summaries of Survival Are Efficient, 603
20.3.5 Percent Change and Waterfall Plots, 605
20.4 Randomized Comparative Trials, 606
20.4.1 Examples of Comparative Trials Used in This Section, 607
20.4.2 Continuous Measures Estimate Treatment Differences, 608
20.4.3 Baseline Measurements Can Increase Precision, 609
20.4.4 Comparing Counts, 610
20.4.5 Nonparametric Survival Comparisons, 612
20.4.6 Risk (Hazard) Ratios and Confidence Intervals Are Clinically Useful Data Summaries, 614
20.4.7 Statistical Models Are Necessary Tools, 615
20.5 Problems With P-Values, 616
20.5.1 P-Values Do Not Represent Treatment Effects, 618
20.5.2 P-Values Do Not Imply Reproducibility, 618
20.5.3 P-Values Do Not Measure Evidence, 619
20.6 Strength of Evidence Through Support Intervals, 620
20.6.1 Support Intervals Are Based on the Likelihood Function, 620
20.6.2 Support Intervals Can Be Used with Any Outcome, 621
20.7 Special Methods of Analysis, 622
20.7.1 The Bootstrap Is Based on Resampling, 623
20.7.2 Some Clinical Questions Require Other Special Methods of Analysis, 623
20.8 Exploratory Analyses, 628
20.8.1 Clinical Trial Data Lend Themselves to Exploratory Analyses, 628
20.8.2 Multiple Tests Multiply Type I Errors, 629
20.8.3 Kinds of Multiplicity, 630
20.8.4 Inevitible Risks from Subgroups, 630
20.8.5 Tale of a Subset Analysis Gone Wrong, 632
20.8.6 Perspective on Subgroup Analyses, 635
20.8.7 Effects the Trial Was Not Designed to Detect, 636
20.8.8 Safety Signals, 637
20.8.9 Subsets, 637
20.8.10 Interactions, 638
20.9 Summary, 639
20.10 Questions for Discussion, 640
21 Prognostic Factor Analyses 644
21.1 Introduction, 644
21.1.1 Studying Prognostic Factors is Broadly Useful, 645
21.1.2 Prognostic Factors Can Be Constant or Time-Varying, 646
21.2 Model-Based Methods, 647
21.2.1 Models Combine Theory and Data, 647
21.2.2 Scale and Coding May Be Important, 648
21.2.3 Use Flexible Covariate Models, 648
21.2.4 Building Parsimonious Models Is the Next Step, 650
21.2.5 Incompletely Specified Models May Yield Biased Estimates, 655
21.2.6 Study Second-Order Effects (Interactions), 656
21.2.7 PFAs Can Help Describe Risk Groups, 656
21.2.8 Power and Sample Size for PFAs, 660
21.3 Adjusted Analyses of Comparative Trials, 661
21.3.1 What Should We Adjust For?, 662
21.3.2 What Can Happen?, 663
21.3.3 Brain Tumor Case Study, 664
21.4 PFAS Without Models, 666
21.4.1 Recursive Partitioning Uses Dichotomies, 666
21.4.2 Neural Networks Are Used for Pattern Recognition, 667
21.5 Summary, 669
21.6 Questions for Discussion, 669
22 Factorial Designs 671
22.1 Introduction, 671
22.2 Characteristics of Factorial Designs, 672
22.2.1 Interactions or Efficiency, But Not Both Simultaneously, 672
22.2.2 Factorial Designs Are Defined by Their Structure, 672
22.2.3 Factorial Designs Can Be Made Efficient, 674
22.3 Treatment Interactions, 675
22.3.1 Factorial Designs Are the Only Way to Study Interactions, 675
22.3.2 Interactions Depend on the Scale of Measurement, 677
22.3.3 The Interpretation of Main Effects Depends on Interactions, 677
22.3.4 Analyses Can Employ Linear Models, 678
22.4 Examples of Factorial Designs, 680
22.5 Partial, Fractional, and Incomplete Factorials, 682
22.5.1 Use Partial Factorial Designs When Interactions Are Absent, 682
22.5.2 Incomplete Designs Present Special Problems, 682
22.6 Summary, 683
22.7 Questions for Discussion, 683
23 Crossover Designs 684
23.1 Introduction, 684
23.1.1 Other Ways of Giving Multiple Treatments Are Not Crossovers, 685
23.1.2 Treatment Periods May Be Randomly Assigned, 686
23.2 Advantages and Disadvantages, 686
23.2.1 Crossover Designs Can Increase Precision, 687
23.2.2 A Crossover Design Might Improve Recruitment, 687
23.2.3 Carryover Effects Are a Potential Problem, 688
23.2.4 Dropouts Have Strong Effects, 689
23.2.5 Analysis is More Complex Than for a Parallel-Group Design, 689
23.2.6 Prerequisites Are Needed to Apply Crossover Designs, 689
23.2.7 Other Uses for the Design, 690
23.3 Analysis, 691
23.3.1 Simple Approaches, 691
23.3.2 Analysis Can Be Based on a Cell Means Model, 692
23.3.3 Other Issues in Analysis, 696
23.4 Classic Case Study, 696
23.5 Summary, 696
23.6 Questions for Discussion, 697
24 Meta-Analyses 698
24.1 Introduction, 698
24.1.1 Meta-Analyses Formalize Synthesis and Increase Precision, 699
24.2 A Sketch of Meta-Analysis Methods, 700
24.2.1 Meta-Analysis Necessitates Prerequisites, 700
24.2.2 Many Studies Are Potentially Relevant, 701
24.2.3 Select Studies, 702
24.2.4 Plan the Statistical Analysis, 703
24.2.5 Summarize the Data Using Observed and Expected, 703
24.3 Other Issues, 705
24.3.1 Cumulative Meta-Analyses, 705
24.3.2 Meta-Analyses Have Practical and Theoretical Limitations, 706
24.3.3 Meta-Analysis Has Taught Useful Lessons, 707
24.4 Summary, 707
24.5 Questions for Discussion, 708
25 Reporting and Authorship 709
25.1 Introduction, 709
25.2 General Issues in Reporting, 710
25.2.1 Uniformity Improves Comprehension, 711
25.2.2 Quality of the Literature, 712
25.2.3 Peer Review Is the Only Game in Town, 712
25.2.4 Publication Bias Can Distort Impressions Based on the Literature, 713
25.3 Clinical Trial Reports, 715
25.3.1 General Considerations, 716
25.3.2 Employ a Complete Outline for Comparative Trial Reporting, 721
25.4 Authorship, 726
25.4.1 Inclusion and Ordering, 727
25.4.2 Responsibility of Authorship, 727
25.4.3 Authorship Models, 728
25.4.4 Some Other Practicalities, 730
25.5 Other Issues in Disseminating Results, 731
25.5.1 Open Access, 731
25.5.2 Clinical Alerts, 731
25.5.3 Retractions, 732
25.6 Summary, 732
25.7 Questions for Discussion, 733
26 Misconduct and Fraud in Clinical Research 734
26.1 Introduction, 734
26.1.1 Integrity and Accountability Are Critically Important, 736
26.1.2 Fraud and Misconduct Are Difficult to Define, 738
26.2 Research Practices, 741
26.2.1 Misconduct May Be Increasing in Frequency, 741
26.2.2 Causes of Misconduct, 742
26.3 Approach to Allegations of Misconduct, 743
26.3.1 Institutions, 744
26.3.2 Problem Areas, 746
26.4 Characteristics of Some Misconduct Cases, 747
26.4.1 Darsee Case, 747
26.4.2 Poisson (NSABP) Case, 749
26.4.3 Two Recent Cases from Germany, 752
26.4.4 Fiddes Case, 753
26.4.5 Potti Case, 754
26.5 Lessons, 754
26.5.1 Recognizing
Steven Piantadosi, MD, PhD, is the Phase One Foundation Distinguished Chair and Director of the Samuel Oschin Cancer Institute, and Professor of Medicine at Cedars-Sinai Medical Center in Los Angeles, California. Dr. Piantadosi is one of the world's leading experts in the design and analysis of clinical trials for cancer research. He has taught clinical trials methods extensively in formal courses and short venues. He has advised numerous academic programs and collaborations nationally regarding clinical trial design and conduct, and has served on external advisory boards for the National Institutes of Health and other prominent cancer programs and centers. The author of more than 260 peer-reviewed scientific articles, Dr. Piantadosi has published extensively on research results, clinical applications, and trial methodology. While his papers have contributed to many areas of oncology, he has also collaborated on diverse studies outside oncology including lung disease and degenerative neurological disease.
