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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…mehr

Produktbeschreibung
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.

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

  • Produktdetails
  • Verlag: John Wiley & Sons
  • Seitenzahl: 928
  • Erscheinungstermin: 9. Oktober 2017
  • Englisch
  • ISBN-13: 9781118959213
  • Artikelnr.: 52551379
Autorenporträt
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 trial 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 degenerativeneurological disease.
Inhaltsangabe
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