# Clinical Trials: A Methodologic Perspective, 3rd Edition

# Clinical Trials: A Methodologic Perspective, 3rd Edition

ISBN: 978-1-118-95920-6

Aug 2017

920 pages

## Description

**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.

This book is accompanied by downloadable files available below under the **DOWNLOADS** tab.

These files include:

**MATHEMATICA**program – A set of downloadable files that tracks the chapters, containing code pertaining to each.

**SAS PROGRAMS and DATA FILES**used in the book.

The following software programs, included in the downloadables, were developed by the author, Steven Piantadosi, M.D., Ph.D:

**RANDOMIZATION**– This program generates treatment assignments for a clinical trial using blocked stratified randomization.

**CRM**– Implements the continual reassessment methods for dose finding clinical trials.

**OPTIMAL**– Calculates two-stage optimal phase II designs using the Simon method.

**POWER**– This is a power and sample size program for clinical trials.

Executables for installing these programs can also be found at https://risccweb.csmc.edu/biostats/.

**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.

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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 Fraud or Misconduct, 754

26.5.2 Misconduct Cases Yield Other Lessons, 756

xxiv CONTENTS

26.6 Clinical Investigators’ Responsibilities, 757

26.6.1 General Responsibilities, 757

26.6.2 Additional Responsibilities Related to INDs, 758

26.6.3 Sponsor Responsibilities, 759

26.7 Summary, 759

26.8 Questions for Discussion, 760

Appendix A Data and Programs 761

A.1 Introduction, 761

A.2 Design Programs, 761

A.2.1 Power and Sample Size Program, 761

A.2.2 Blocked Stratified Randomization, 763

A.2.3 Continual Reassessment Method, 763

A.2.4 Envelope Simulation, 763

A.3 Mathematica Code, 763

AppendixB Abbreviations 764

AppendixC Notation and Terminology 769

C.1 Introduction, 769

C.2 Notation, 769

C.2.1 Greek Letters, 770

C.2.2 Roman Letters, 771

C.2.3 Other Symbols, 772

C.3 Terminology and Concepts, 772

Appendix D Nuremberg Code 788

D.1 Permissible Medical Experiments, 788

References 790

Index 871