1.2 Audience and Scope, 21.3 Other Sources of Knowledge, 4 1.3.1 Terminology, 5 1.3.2 Review of Notation and Terminology Is Helpful, 6 1.4 Examples, Data, and Programs, 6 2.1 Introductio
Trang 3Established by WALTER A SHEWHART and SAMUEL S WILKS
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A complete list of the titles in this series appears at the end of this volume.
Trang 4CLINICAL TRIALS
A Methodologic Perspective Second Edition
STEVEN PIANTADOSI
Johns Hopkins School of Medicine, Baltimore, MD
A JOHN WILEY & SONS, INC., PUBLICATION
Trang 5Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
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Piantadosi, Steven.
Clinical trials : a methodologic perspective / Steven Piantadosi—2nd ed.
p ; cm – (Wiley series in probability and statistics)
Includes bibliographical references and index.
ISBN-13: 978-0-471-72781-1 (cloth : alk paper)
ISBN-10: 0-471-72781-4 (cloth : alk paper)
1 Clinical trials—Statistical methods I Title II Series.
[DNLM: 1 Biomedical Research—methods 2 Research—methods 3 Clinical
Trang 61.2 Audience and Scope, 2
1.3 Other Sources of Knowledge, 4
1.3.1 Terminology, 5
1.3.2 Review of Notation and Terminology Is Helpful, 6
1.4 Examples, Data, and Programs, 6
2.1 Introduction, 9
2.1.1 Clinical Reasoning Is Based on the Case History, 10
2.1.2 Statistical Reasoning Emphasizes Inference Based on Designed
Data Production, 122.1.3 Clinical and Statistical Reasoning Converge in Research, 13
2.2 Defining Clinical Trials Formally, 14
2.2.1 Mixing of Clinical and Statistical Reasoning Is Recent, 14
2.2.2 Clinical Trials Are Rigorously Defined, 16
2.2.3 Experiments Can Be Misunderstood, 17
2.2.4 Clinical Trials as Science, 18
2.2.5 Trials and Statistical Methods Fit within a Spectrum of
Clinical Research, 19
v
Trang 72.3 Practicalities of Usage, 20
2.3.1 Predicates for a Trial, 20
2.3.2 Trials Can Provide Confirmatory Evidence, 20
2.3.3 Clinical Trials Are Unwieldy, Messy, and Reliable, 21
2.3.4 Other Methods Are Valid for Making Some Clinical
Inferences, 232.3.5 Trials Are Difficult to Apply in Some Circumstances, 25
2.3.6 Randomized Studies Can Be Initiated Early, 25
2.4 Summary, 26
2.5 Questions for Discussion, 26
3.1 Introduction, 29
3.1.1 Science and Ethics Share Objectives, 30
3.1.2 Equipoise and Uncertainty, 31
3.2 Duality, 32
3.2.1 Clinical Trials Sharpen, but Do Not Create, the Issue, 32
3.2.2 A Gene Therapy Tragedy Illustrates Duality, 32
3.2.3 Research and Practice Are Convergent, 33
3.2.4 The Hippocratic Tradition Does Not Proscribe Clinical
Trials, 363.2.5 Physicians Always Have Multiple Roles, 38
3.3 Historically Derived Principles of Ethics, 41
3.3.1 Nuremberg Contributed an Awareness of the Worst Problems, 413.3.2 High-Profile Mistakes Were Made in the United States, 42
3.3.3 The Helsinki Declaration Was Widely Adopted, 42
3.3.4 Other International Guidelines Have Been Proposed, 44
3.3.5 Institutional Review Boards Provide Ethical Oversight, 45
3.3.6 Ethical Principles Relevant to Clinical Trials, 46
3.4 Contemporary Foundational Principles, 48
3.5.1 Practice Based on Unproven Treatments Is Not Ethical, 53
3.5.2 Ethics Considerations Are Important Determinants of Design, 553.5.3 Specific Methods Have Justification, 57
Trang 83.6 Professional Conduct, 59
3.6.1 Conflict of Interest, 59
3.6.2 Professional Statistical Ethics, 61
3.7 Summary, 63
3.8 Questions for Discussion, 63
4.1 Introduction, 65
4.1.1 Some Ways to Learn about Trials in a Given Context, 66
4.1.2 Issues of Context, 67
4.2 Drugs, 68
4.2.1 Are Drugs Special?, 70
4.2.2 Why Trials Are Used Extensively for Drugs, 71
4.3 Devices, 72
4.3.1 Use of Trials for Medical Devices, 73
4.3.2 Are Devices Different from Drugs?, 74
4.4.4 Methodology and Framework for Prevention Trials, 81
4.5 Complementary and Alternative Medicine, 82
4.5.1 The Essential Paradox of CAM and Clinical Trials, 84
4.5.2 Why Trials Have Not Been Used Extensively in CAM, 85
4.5.3 Some Principles for Rigorous Evaluation, 87
4.6 Surgery and Skill-Dependent Therapies, 88
4.6.1 Why Trials Have Not Been Used Extensively in Surgery, 90
4.6.2 Reasons Why Some Surgical Therapies Require Less
Rig-orous Study Designs, 924.6.3 Sources of Variation, 92
4.6.4 Difficulties of Inference, 93
4.6.5 Control of Observer Bias Is Possible, 94
4.6.6 Illustrations from an Emphysema Surgery Trial, 95
4.7 A Brief View of Some Other Contexts, 101
Trang 95.2 Differences in Statistical Perspectives, 108
5.2.1 Models and Parameters, 108
5.2.2 Philosophy of Inference Divides Statisticians, 108
5.6.1 Statistical Procedures Are Not Standardized, 123
5.6.2 Practical Controversies Related to Statistics Exist, 123
5.8 Questions for Discussion, 125
6.1 Introduction, 127
6.1.1 Trials Are Relatively Simple Experimental Designs, 128
6.1.2 Study Design Is Critical for Inference, 129
6.2 Goals of Experimental Design, 129
6.2.1 Control of Random Error and Bias Is the Goal, 129
6.2.2 Conceptual Simplicity Is Also a Goal, 130
Types of Trials, 1336.4 Design Concepts, 134
6.4.1 The Foundations of Design Are Observation and Theory, 1346.4.2 A Lesson from the Women’s Health Initiative, 136
6.4.3 Experiments Use Three Components of Design, 137
6.5 Survey of Developmental Trial Designs, 143
6.5.1 Early Development, 143
6.5.2 Middle Development, 144
6.5.3 Late Development, 148
Trang 106.6 Special Design Issues, 151
6.7 Importance of the Protocol Document, 157
6.7.1 Protocols Have Many Functions, 158
6.7.2 Deviations from Protocol Specifications Are Common, 159
6.7.3 Protocols Are Structured, Logical, and Complete, 160
6.9 Questions for Discussion, 165
7.1 Introduction, 167
7.2 Random Error, 169
7.2.1 Hypothesis Tests versus Significance Tests, 169
7.2.2 Hypothesis Tests Are Subject to Two Types of Random
Error, 1707.2.3 Type I Errors Are Relatively Easy to Control, 171
7.2.4 The Properties of Confidence Intervals Are Similar, 171
7.2.5 Using a One- or Two-Sided Hypothesis Test Is Not the
Right Question, 1727.2.6 P -Values Quantify the Type I Error, 173
7.2.7 Type II Errors Depend on the Clinical Difference of
Interest, 1737.2.8 Post hoc Power Calculations Are not Helpful, 175
7.3 Clinical Biases, 176
7.3.1 Relative Size of Random Error and Bias Is Important, 176
7.3.2 Bias Arises from Numerous Sources, 176
7.3.3 Controlling Structural Bias Is Conceptually Simple, 179
7.4 Statistical Bias, 182
7.4.1 Some Statistical Bias Can Be Corrected, 183
7.4.2 Unbiasedness Is Not the Only Desirable Attribute of an
Estimator, 183
7.6 Questions for Discussion, 185
8.1 Introduction, 187
Trang 118.2 Objectives, 188
8.2.1 Estimation Is the Most Common Objective, 188
8.2.2 Selection Can Also Be an Objective, 189
8.2.3 Objectives Require Various Scales of Measurement, 189
8.3 Outcomes, 190
8.3.1 Mixed Outcomes and Predictors, 190
8.3.2 Criteria for Evaluating Outcomes, 191
8.3.3 Prefer “Hard” or Objective Outcomes, 191
8.3.4 Outcomes Can Be Quantitative or Qualitative, 192
8.3.5 Measures Are Useful and Efficient Outcomes, 192
8.3.6 Some Outcomes Are Summarized as Counts, 193
8.3.7 Ordered Categories Are Commonly Used for Severity or
Toxicity, 1938.3.8 Unordered Categories Are Sometimes Used, 193
8.3.9 Dichotomies Are Simple Summaries, 194
8.3.10 Event Times May Be Censored, 194
8.3.11 Event Time Data Require Two Numerical Values, 196
8.3.12 Censoring and Lost to Follow-up Are Not the Same, 197
8.3.13 Survival and Disease Progression, 198
8.3.14 Composite Outcomes Instead of Censoring, 199
8.3.15 Waiting for Good Events Complicates Censoring, 199
8.4 Surrogate Outcomes, 200
8.4.1 Surrogate Outcomes Are Disease-Specific, 201
8.4.2 Surrogate Outcomes Can Make Trials More Efficient, 204
8.4.3 Surrogate Outcomes Have Significant Limitations, 205
8.5 Some Special Endpoints, 207
8.5.1 Repeated Measurements Are not Common in Clinical
Tri-als, 2078.5.2 Patient Reported Outcomes, 207
8.7 Questions for Discussion, 209
9.1 Introduction, 211
9.1.1 Translational Setting and Outcomes, 212
9.1.2 Character and Definition, 213
9.1.3 Small Does Not Mean Translational, 214
9.2 Information from Translational Trials, 214
9.2.1 Parameter Uncertainty versus Outcome Uncertainty, 214
Trang 1210.2.1 What Does “Phase I” Mean?, 224
10.2.2 Distinguish Dose–Safety from Dose–Efficacy, 226
10.2.3 Dose Optimality Is a Design Definition, 226
10.2.4 The General Dose-Finding Problem Is Unsolved, 227
10.2.5 Unavoidable Subjectivity, 228
10.2.6 Sample Size Is an Outcome of Dose-Finding Studies, 229
10.2.7 Idealized Dose-Finding Design, 229
10.3 Fibonacci and Related Dose-Ranging, 230
10.3.1 Some Historical Designs, 231
10.3.2 Typical Design, 231
10.3.3 Operating Characteristics Can Be Calculated, 232
10.3.4 Modifications, Strengths, and Weaknesses, 234
10.4 Designs Used for Dose-Finding, 236
10.4.1 Mathematical Models Facilitate Inferences, 236
10.4.2 Continual Reassessment Method, 237
10.4.3 Pharmacokinetic Measurements Might Be Used to Improve
CRM Dose Escalations, 24010.4.4 The CRM Is an Attractive Design to Criticize, 241
10.4.5 CRM Example, 241
10.4.6 Can Randomization Be Used in Phase I or TM Trials?, 242
10.4.7 Phase I Data Have Other Uses, 242
10.5 More General Dose-Finding Issues, 242
10.5.1 Dose-Finding Is Not Always One Dimensional, 243
10.5.2 Dual Dose-Finding, 244
10.5.3 Optimizing Safety and Efficacy Jointly, 247
10.6 Summary, 250
10.7 Questions for Discussion, 250
Trang 1311.3 Early Developmental Trials, 256
11.3.1 Translational Trials, 256
11.3.2 Dose-Finding Trials, 258
11.4 Safety and Activity Studies, 258
11.4.1 Simple SA Designs Can Use Fixed Sample Size, 25911.4.2 Exact Binomial Confidence Limits Are Helpful, 260
11.4.3 Bayesian Binomial Confidence Intervals, 263
11.4.4 A Bayesian Approach Can Use Prior Information, 26411.4.5 Likelihood-Based Approach for Proportions, 266
11.4.6 Confidence Intervals for a Mean Provide a Sample Size
Approach, 26711.4.7 Confidence Intervals for Event Rates Can Determine
Sample Size, 26811.4.8 Likelihood-Based Approach for Event Rates, 271
11.4.9 Ineffective or Unsafe Treatments Should Be Discarded
Early, 27111.4.10 Two-Stage Designs Are Efficient, 272
11.4.11 Randomized SA Trials, 273
11.5 Comparative Trials, 277
11.5.1 How to Choose Type I and II Error Rates, 277
11.5.2 Comparisons Using the t -Test Are a Good Learning
Example, 27811.5.3 Likelihood-Based Approach, 280
11.5.4 Dichotomous Responses Are More Complex, 282
11.5.5 Hazard Comparisons Yield Similar Equations, 283
11.5.6 Parametric and Nonparametric Equations Are Connected, 28611.5.7 Accommodating Unbalanced Treatment Assignments, 28611.5.8 A Simple Accrual Model Can Also Be Incorporated, 28811.5.9 Noninferiority, 290
11.7.3 Increase the Sample Size for Nonadherence, 299
11.7.4 Simulated Lifetables Can Be a Simple Design Tool, 30111.7.5 Sample Size for Prognostic Factor Studies, 302
11.7.6 Computer Programs Simplify Calculations, 303
11.7.7 Simulation Is a Powerful and Flexible Design Alternative, 30411.7.8 Power Curves are Sigmoid Shaped, 304
11.8 Summary, 305
11.9 Questions for Discussion, 306
Trang 1412 The Study Cohort 309
12.1 Introduction, 309
12.2 Defining the Study Cohort, 310
12.2.1 Active Sampling or Enrichment, 310
12.2.2 Participation May Select Subjects with Better Prognosis, 31112.2.3 Define the Study Population Using Eligibility and
Exclusion Criteria, 31412.2.4 Quantitative Selection Criteria versus False Precision, 316
12.2.5 Comparative Trials Are Not Sensitive to Selection, 317
12.3 Anticipating Accrual, 318
12.3.1 Using a Run-in Period, 318
12.3.2 Estimate Accrual Quantitatively, 319
12.4 Inclusiveness, Representation, and Interactions, 322
12.4.1 Inclusiveness Is a Worthy Goal, 322
12.4.2 Barriers Can Hinder Trial Participation, 322
12.4.3 Efficacy versus Effectiveness Trials, 323
12.4.4 Representation: Politics Blunders into Science, 324
13.2.3 Simple Randomization Can Yield Imbalances, 335
13.3 Constrained Randomization, 336
13.3.1 Blocking Improves Balance, 336
13.3.2 Blocking and Stratifying Balances Prognostic Factors, 337
13.3.3 Other Considerations Regarding Blocking, 339
13.4 Adaptive Allocation, 340
13.4.1 Urn Designs Also Improve Balance, 340
13.4.2 Minimization Yields Tight Balance, 341
13.4.3 Play the Winner, 342
13.5 Other Issues Regarding Randomization, 343
13.5.1 Administration of the Randomization, 343
13.5.2 Computers Generate Pseudorandom Numbers, 345
13.5.3 Randomization Justifies Type I Errors, 345
13.6 Unequal Treatment Allocation, 349
13.6.1 Subsets May Be of Interest, 350
13.6.2 Treatments May Differ Greatly in Cost, 350
13.6.3 Variances May Be Different, 350
Trang 1513.7 Randomization before Consent, 351
13.8 Summary, 352
13.9 Questions for Discussion, 352
14.1 Introduction, 355
14.1.1 Motives for Monitoring, 356
14.1.2 Components of Responsible Monitoring, 357
14.1.3 Trials Can Be Stopped for a Variety of Reasons, 357
14.1.4 There Is Tension in the Decision to Stop, 358
14.2 Administrative Issues in Trial Monitoring, 359
14.2.1 Monitoring of Single-Center Studies Relies on Periodic
Investigator Reporting, 36014.2.2 Composition and Organization of the TEMC, 361
14.2.3 Complete Objectivity Is Not Ethical, 364
14.3 Organizational Issues Related to Data, 366
14.3.1 The TEMC Assesses Baseline Comparability, 366
14.3.2 The TEMC Reviews Accrual and Expected Time to Study
Completion, 36714.3.3 Timeliness of Data and Reporting Lags, 367
14.3.4 Data Quality Is a Major Focus of the TEMC, 368
14.3.5 The TEMC Reviews Safety and Toxicity Data, 368
14.3.6 Efficacy Differences Are Assessed by the TEMC, 369
14.3.7 The TEMC Should Address a Few Practical Questions
Specifically, 36914.3.8 The TEMC Mechanism Has Potential Weaknesses, 371
14.4 Statistical Methods for Monitoring, 371
14.4.1 There Are Several Approaches to Evaluating Incomplete
Evidence, 37114.4.2 Likelihood Methods, 373
14.6 Questions for Discussion, 393
15.1 Introduction, 395
15.2 Nature of Some Specific Data Imperfections, 396
15.2.1 Evaluability Criteria Are a Methodologic Error, 397
Trang 1615.2.2 Statistical Methods Can Cope with Some Types of Missing
Data, 39815.2.3 Protocol Nonadherence Is Common, 400
15.3 Treatment Nonadherence, 402
15.3.1 Intention to Treat Is a Policy of Inclusion, 402
15.3.2 Coronary Drug Project Results Illustrate the Pitfalls of
Exclusions based on Nonadherence, 40315.3.3 Statistical Studies Support the ITT Approach, 403
15.3.4 Trials Can Be Viewed as Tests of Treatment Policy, 404
15.3.5 ITT Analyses Cannot Always Be Applied, 404
15.3.6 Trial Inferences Depend on the Experimental Design, 406
15.4 Summary, 406
15.5 Questions for Discussion, 407
16.1 Introduction, 409
16.1.1 Structure Aids Data Interpretation, 410
16.1.2 Estimates of Risk Are Natural and Useful, 411
16.2 Dose-Finding and PK Trials, 412
16.2.1 Pharmacokinetic Models Are Essential for Analyzing DF
Trials, 41216.2.2 A Two-Compartment Model Is Simple but Realistic, 413
16.2.3 PK Models Are Used by “Model Fitting”, 416
16.3 SA Studies, 417
16.3.1 Mesothelioma Clinical Trial Example, 417
16.3.2 Summarize Risk for Dichotomous Factors, 418
16.3.3 Nonparametric Estimates of Survival
Are Robust, 42016.3.4 Parametric (Exponential) Summaries of Survival Are Effi-
cient, 42116.4 Comparative Efficacy Trials (Phase III), 423
16.4.1 Examples of CTE Trials Used in This Section, 424
16.4.2 Continuous Measures Estimate Treatment Differences, 426
16.4.3 Baseline Measurements Can Increase Precision, 426
16.4.4 Nonparametric Survival Comparisons, 427
16.4.5 Risk (Hazard) Ratios and Confidence Intervals Are
Clini-cally Useful Data Summaries, 42916.4.6 Statistical Models Are Helpful Tools, 431
16.4.7 P -Values Do Not Measure Evidence, 432
16.5 Strength of Evidence through Support Intervals, 434
16.5.1 Support Intervals Are Based on the Likelihood Function, 43416.5.2 Support Intervals Can Be Used with Any Outcome, 435
16.6 Special Methods of Analysis, 436
Trang 1716.6.1 The Bootstrap Is Based on Resampling, 437
16.6.2 Some Clinical Questions Require Other Special Methods
of Analysis, 43816.7 Exploratory or Hypothesis-Generating Analyses, 442
16.7.1 Clinical Trial Data Lend Themselves to Exploratory
Anal-yses, 44216.7.2 Multiple Tests Multiply Type I Errors, 442
16.7.3 Kinds of Multiplicity, 443
16.7.4 Subgroup Analyses Are Error Prone, 443
16.8 Summary, 446
16.9 Questions for Discussion, 447
17.1 Introduction, 453
17.1.1 Studying Prognostic Factors Is Broadly Useful, 454
17.1.2 Prognostic Factors Can Be Constant or Time-Varying, 455
17.2 Model-Based Methods, 456
17.2.1 Models Combine Theory and Data, 456
17.2.2 The Scale of Measurements (Coding) May Be Important, 45717.2.3 Use Flexible Covariate Models, 457
17.2.4 Building Parsimonious Models Is the Next Step, 459
17.2.5 Incompletely Specified Models May Yield Biased Estimates, 46417.2.6 Study Second-Order Effects (Interactions), 465
17.2.7 PFAs Can Help Describe Risk Groups, 466
17.2.8 Power and Sample Size for PFAs, 470
17.3 Adjusted Analyses of Comparative Trials, 470
17.3.1 What Should We Adjust For?, 471
17.3.2 What Can Happen?, 472
17.4 Non–Model-Based Methods for PFAs, 475
17.4.1 Recursive Partitioning Uses Dichotomies, 475
17.4.2 Neural Networks Are Used for Pattern Recognition, 476
17.5 Summary, 477
17.6 Questions for Discussion, 478
18.1 Introduction, 479
18.2 General Issues in Reporting, 480
18.2.1 Uniformity of Reporting Can Improve Comprehension, 481
18.2.2 Quality of the Literature, 482
18.2.3 Peer Review Is the Only Game in Town, 482
18.2.4 Publication Bias Can Distort Impressions Based on the
Literature, 48318.3 Clinical Trial Reports, 484
Trang 1818.4.4 Some Other Practicalities, 498
18.5 Alternative Ways to Disseminate Results, 499
18.6 Summary, 499
18.7 Questions for Discussion, 500
19.1 Introduction, 501
19.2 Characteristics of Factorial Designs, 502
19.2.1 Interactions or Efficiency, but Not Both Simultaneously, 50219.2.2 Factorial Designs Are Defined by Their Structure, 502
19.2.3 Factorial Designs Can Be Made Efficient, 504
19.4 Examples of Factorial Designs, 509
19.5 Partial, Fractional, and Incomplete Factorials, 511
19.5.1 Use Partial Factorial Designs When Interactions Are Absent, 51119.5.2 Incomplete Designs Present Special Problems, 512
20.2 Advantages and Disadvantages, 517
20.2.1 Crossover Designs Can Increase Precision, 517
20.2.2 A Crossover Design Might Improve Recruitment, 518
20.2.3 Carryover Effects Are a Potential Problem, 518
20.2.4 Dropouts Have Strong Effects, 519
20.2.5 Analysis Is More Complex Than Parallel-Groups Designs, 51920.2.6 Prerequisites Are Needed to Apply Crossover Designs, 520
20.3 Analysis, 520
Trang 1920.3.1 Analysis Can Be Based on a Cell Means Model, 521
20.3.2 Other Issues in Analysis, 525
20.3.3 Classic Case Study, 525
21.2.1 Meta-Analysis Necessitates Prerequisites, 531
21.2.2 Many Studies Are Potentially Relevant, 532
21.2.3 Select Studies, 533
21.2.4 Plan the Statistical Analysis, 533
21.2.5 Summarize the Data Using Observed and Expected, 534
21.3 Other Issues, 536
21.3.1 Meta-Analyses Have Practical and Theoretical Limitations, 53621.3.2 Meta-Analysis Has Taught Useful Lessons, 537
21.4 Summary, 537
21.5 Questions for Discussion, 538
22.1 Introduction, 539
22.1.1 Integrity and Accountability Are Critically Important, 540
22.1.2 Fraud and Misconduct Are Difficult to Define, 541
22.4.2 Poisson (NSABP) Case, 553
22.4.3 Two Recent Cases from Germany, 556
22.5 Lessons, 557
22.5.1 Recognizing Fraud or Misconduct, 557
22.5.2 Misconduct Cases Yield Other Lessons, 559
22.6 Clinical Investigators’ Responsibilities, 560
22.6.1 General Responsibilities, 560
22.6.2 Additional Responsibilities Related to INDs, 561
22.6.3 Sponsor Responsibilities, 562
Trang 20A.3 Design Programs, 566
A.3.1 Power and Sample Size Program, 566
A.3.2 Blocked Stratified Randomization, 567
A.3.3 Continual Reassessment Method, 567
A.4 Mathematica Code, 567
E.2 Basic Principles for All Medical Research, 594
F.1 Introduction, 599
F.2 Responsibility for Data and Safety Monitoring, 599
F.3 Requirement for Data and Safety Monitoring Boards, 600
F.4 Responsibilities of the DSMB, 600
F.5 Membership, 600
F.6 Meetings, 601
F.7 Recommendations from the DSMB, 601
F.8 Release of Outcome Data, 602
F.9 Confidentiality Procedures, 602
F.10 Conflict of Interest, 602
Trang 21G NIH Data and Safety Monitoring Policy 605
G.1 Background, 605
G.2 Principles of Monitoring Data and Safety, 606
G.3 Practical and Implementation Issues: Oversight of Monitoring, 606
G.4 Institutes and Centers Responsibilities, 606
G.5 Performance of Data and Safety Monitoring, 607
G.6 Examples of Monitoring Operations, 608
H.1 Introduction, 609
H.2 Constitutional Authority, 609
H.3 Rules of Professional Conduct, 609
H.3.1 The Public Interest, 610
H.3.2 Duty to Employers and Clients, 610
H.3.3 Duty to the Profession, 610
H.3.4 Disciplinary Procedures, 611
Trang 22A respectable period of time has passed since the first edition of this book, during whichseveral pressures have necessitated a second edition Most important, there were theinadvertent errors needing correction The field has also had some significant changes,not so much in methodology perhaps as in context, regulation, and the like I can saysome things more clearly now than previously because many issues are better defined
or I care more (or less) about how the discussion will be received
I have added much new material covering some gaps (and therefore some newmistakes), but always hoping to make learning easier The result is too much to cover
in the period that I usually teach—one academic quarter It may be appropriate for asemester course Many students tell me that they consult this book as a reference, sothe extra material should ultimately be useful
Many colleagues have been kind enough to give their valuable time to review drafts
of chapters, despite apparently violating Nabokov’s advice: “Only ambitious ties and hearty mediocrities exhibit their rough drafts It is like passing around samples
nonenti-of one’s sputum.” Nevertheless, I am grateful to Elizabeth Garrett-Mayer, Ph.D., nie Piantadosi, M.S.W, M.H.S., Anne Piantadosi, Irene Roach, Pamela Scott, Ph.D.,Gail Weinmann, M.D., and Xiaobu Ye, M.D., M.S for such help Chris Szekely,Ph.D reviewed many chapters and references in detail Sean Roach, M.L.S and AlisaMoore provided valuable assistance with references Alla Guseynova, M.S reviewedand corrected the computational code for the book
Bon-As usual, students in my class Design and Analysis of Clinical Trials offered through
the Hopkins Department of Biostatistics and the Graduate Training Program in ClinicalInvestigation provide the best motivation for writing this book by virtue of their livelydiscussion and questions I would also like to thank the faculties, students, and spon-sors from the AACR/ASCO Vail Workshop, as well as the sister workshops, FECS inFlims, Switzerland, and ACORD in Cairns, Australia, who have provided many prac-tical questions and examples for interesting clinical trial designs over the years Suchteaching venues require conciseness and precision from a clinical trialist, and illustratethe very heart of collaborative research
xxi
Trang 23Finally there are two institutional venues that have helped motivate and shape mywriting regarding clinical trials One is the Protocol Review and Monitoring Committee
in the Cancer Center, a scientific review forum on which I have served for manyyears The second is the Institutional Review Board, on which I have more recentlybegun to serve My colleagues in both of these settings encourage and take a careful,constructive, and detailed view of a multitude of diverse trials, and have taught me agreat deal
Steven Piantadosi
Baltimore, Maryland, 2005
Trang 24Books are fatal: they are the curse of the human race Nine-tenths of existing books are nonsense, and the clever books are the refutation of that nonsense The greatest misfortune that ever befell man was the invention of printing [Benjamin Disraeli]
Writing is an adventure To begin with, it is a toy and an amusement Then it becomes
a mistress, then it becomes a master, then it becomes a tyrant The last phase is that just
as you are about to be reconciled to your servitude, you kill the monster and fling him to the public [Winston Churchill]
Writing is easy; all you do is sit staring at a blank sheet of paper until the drops of blood form on your forehead [Gene Fowler]
Trang 25PREFACE TO THE FIRST EDITION
In recent years a great deal has been written about clinical trials and closely related areas
of biostatistics, biomathematics, biometry, epidemiology, and clinical epidemiology.The motive for writing this book is that there still seems to be a need among bothphysicians and biostatisticians for direct, relevant accounts of basic statistical methods
in clinical trials The need for both trialists and clinical investigators to learn about goodmethodology is particularly acute in oncology, where investigators search for treatmentadvances of great clinical importance, but modest size relative to the variability and biaswhich characterize studies of human disease A similar need with the same motivationexists in many other diseases
On the medical side of clinical trials, the last few years have seen a sharpened focus
on training of clinical investigators in research methods Training efforts have rangedfrom short intensive courses to research fellowships lasting years and culminating in
a postgraduate degree The evolution of teaching appears to be toward defining aspecialty in clinical research The material in this book should be of interest to thosewho take this path The technical subjects may seem difficult at first, but the clinicianshould soon become comfortable with them
On the biostatistical side of clinical trials, there has been a near explosion of methods
in recent years However, this is not a book on statistical theory Readers with agood foundation in biostatistics should find the technical subjects practical and quiteaccessible It is my hope that such students will see some cohesiveness to the field, fill
in gaps in their knowledge, and be able to explore areas such as ethics and misconductthat are important to clinical trials
There are some popular perceptions about clinical trials to which this book does notsubscribe For example, some widely used terminology regarding trials is unhelpfuland I have attempted to counteract it by proposing alternatives Also noncomparativetrial designs (e.g., early developmental studies) are often inappropriately excluded fromdiscussions of methods I have tried to present concepts that unify all designed studiesrather than ideas that artificially distinguish them Dealing with pharmacokinetic-based
xxv
Trang 26designs tends to complicate some of the mathematics, but the concepts are essentialfor understanding these trials.
The book is intended to provide at least enough material for the core of a semester course on clinical trials In research settings where trials are used, the audiencefor such a course will likely have varying skills and directions However, with abackground of basic biostatistics, an introductory course in clinical trials or researchmethods, and appropriate didactic discussion, the material presented here should beuseful to a heterogeneous group of students
half-Many individuals have contributed to this book in indirect, but significant ways
I am reminded of two colleagues, now deceased, who helped to shape my thinkingabout clinical trials David P Byar, M.D nurtured my early academic and quantitativeinterest in clinical trials in the early 1980s at the National Institutes of Health After
I joined the Johns Hopkins School of Medicine, Brigid G Leventhal, M.D showed
me a mature, compassionate, and rigorous view of clinical trials from a practitioner’sperspective I hope the thoughts in this book reflect some of the good attitudes andvalues of these fine scholars
Other colleagues have taught me many lessons regarding clinical trials through theirwritings, lectures, conversations, and willingness to answer many questions I wouldparticularly like to thank Mitchell H Gail, M.D., Ph.D and Curtis L Meinert, Ph.D formuch valuable advice and good example over the years One of the most worthwhileexperiences that a trial methodologist can have is to review and influence the designs
of clinical trials while they are being developed My colleagues at Johns Hopkins havecooperated in this regard through the Oncology Center’s Clinical Research Committee,especially Hayden Braine, M.D., who has chaired the Committee wisely for many years.Through long-standing collaborations with the Lung Cancer Study Group, I metmany clinical scholars with much to say and teach about trials I would like to thankthem, especially E Carmack Holmes, M.D., John C Ruckdeschel, M.D., and RobertGinzberg, M.D for showing me a model of interdisciplinary collaboration and friend-ship, which continued to outlive the financial arrangements Recent collaborations withcolleagues in the New Approaches to Brain Tumor Therapy Consortium have alsoenhanced my appreciation and understanding of early developmental trials
In recent months many colleagues have assisted me by reading and offering ments on drafts of the chapters that follow For this help, I would like to thank LinaAsmar, Ph.D., Tatiana Barkova, Ph.D., Jeanne DeJoseph, Ph.D., C.N.M., R.N., SuzanneDibble, D.N.Sc., R.N., James Grizzle, Ph.D., Curt Meinert, Ph.D., Mitch Gail, M.D.,Ph.D., Barbara Hawkins, Ph.D., Steven Goodman, M.D., Ph.D., Cheryl Enger, Ph.D.,Guanghan Liu, Ph.D., J Jack Lee, Ph.D., Claudia Moy, Ph.D., John O’Quigley, Ph.D.,Thomas F Pajak, Ph.D., Charles Rohde, Ph.D., Barbara Starklauf, M.A.S., Manel C.Wijesinha, Ph.D., and Marianna Zahurak, M.S Many of the good points belong tothem—the errors are mine
com-Students in my classes on the Design and Analysis of Clinical Trials and Design of Experiments at the Johns Hopkins School of Hygiene and Public Health have con-
tributed to this book by working with drafts, making helpful comments, workingproblems, or just discussing particular points I would especially like to thank MariaDeloria, Kathleen Weeks, Ling-Yu Ruan, and Jeffrey Blume for their input HelenCromwell and Patty Hubbard have provided a great deal of technical assistance withthe preparation of the manuscript Gary D Knott, Ph.D., Barry J Bunow, Ph.D., andthe staff at Civilized Software, Bethesda, Maryland (www.civilized.com) furnished me
Trang 27with MLAB software, without which many tasks in the following pages would bedifficult.
This book was produced in LATEX using Scientific Workplace version 2.5 I am
indebted to Kathy Watt of TCI Software Research in Las Cruces, New Mexico,(www.tcisoft.com) for assistance in preparing the style A special thanks goes to IreneRoach for editing an early draft of the manuscript and to Sean Roach for collectingand translating hard-to-find references
It is not possible to write a book without stealing a large amount of time from one’sfamily Bonnie, Anne L., and Steven T not only permitted this to happen but also weresupportive, helpful, and understood why I felt it was necessary I am most grateful tothem for their patience and understanding throughout this project
Steven Piantadosi
Baltimore, Maryland,
March 1997
Trang 28This book is not intended to be an introduction to clinical trials It should be part
of a one- or two-quarter second structured postgraduate course for an audience withquantitative skills and a biological focus The first edition evolved over a dozen yearsfrom the merging of two courses: one in experimental design and one in clinical trials.This second edition is the result of seven additional years of teaching and concomitantchanges in the field The book assumes a working knowledge of basic biostatistics andsome familiarity with clinical trials, either didactic or practical It is also helpful ifthe reader understands some more advanced statistical concepts, especially lifetables,survival models, and likelihoods I recognize that clinicians often lack this knowledge.However, many contemporary medical researchers are seeking the required quantitativebackground through formal training in clinical investigation methods or experimentaltherapeutics No clinical knowledge is needed to understand the concepts in this book,although it will be helpful throughout
Many readers of this book will find the discussion uneven, ranging from basic totechnically complex This is partly a consequence of the very nature of clinical trials and
Clinical Trials: A Methodologic Perspective, 2E, by S Piantadosi
Copyright 2005 John Wiley & Sons, Inc.
1
Trang 29partly the result of trying to address a heterogeneous population of students My classestypically contain an equal mixture of biostatistics graduate students, medical doctors
in specialty or subspecialty training (especially working toward a degree in clinicalinvestigation), and other health professionals training to be sophisticated managers orconsumers of clinical trials For such an audience the goal is to provide breadth and
to write so as not to be misunderstood
This book should be supplemented with lecture and discussion, and possibly acomputer lab The reader who does not have an opportunity for formal classroomdialogue will need to explore the references more extensively Exercises and discussionquestions are provided at the end of each chapter Most are intentionally made open-ended, with a suggestion that the student answer them in the form of a one- or two-pagememorandum, as though providing an expert opinion to less-experienced investigators
1.2 AUDIENCE AND SCOPE
The audience for this book is clinical trialists It is not a simple matter to define aclinical trialist, but operationally it is someone who is immersed in the science oftrials Being a truly interdisciplinary field, trialists can be derived from a number ofsources: (1) quantitative or biostatistical, (2) administrative or managerial, (3) clinical,
or (4) ethical Therefore students can approach the subject primarily from any of theseperspectives
It is common today for rigorous trialists to be strongly statistical This is because ofthe fairly rapid recent pace of methods for clinical trials coming from that field, andalso because statistics pertains to all of the disciplines in which trials are conducted.However, the discussion in this book does not neglect the other viewpoints that arealso essential to understanding trials Many examples will relate to cancer because that
is the primary field in which I work, but the concepts will generalize to other areas.Scientists who specialize in clinical trials are frequently dubbed “statisticians.” I willsometimes use that term with the following warning regarding rigor: statistics is an oldand broad profession There is not a one-to-one correspondence between statisticians orbiostatisticians and knowledge of clinical trials However, trial methodologists, whetherstatisticians or not, are likely to know a lot about biostatistics and will be accustomed
to working with statistical experts Many trial methodologists are not statisticians at all,but evolve from epidemiologists or clinicians with a strongly quantitative orientation,
as indicated above
I have made an effort to delineate and emphasize principles common to all types oftrials: translational, developmental, safety, comparative, and large-scale studies Thisfollows from a belief that it is more helpful to learn about the similarities amongtrials rather than differences However, it is unavoidable that distinctions must bemade and the discussion tailored to specific types of studies I have tried to keep suchdistinctions, which are often artificial, to a minimum Various clinical contexts alsotreat trials differently, a topic discussed briefly in Chapter 4
There are many important aspects of clinical trials not covered here in any detail.These include administration, funding, conduct, quality control, and the considerableinfrastructure necessary to conduct trials These topics might be described as the tech-nology of trials, whereas my intent is to focus on the science of trials Technology
is vitally important, but falls outside of the scope of this book Fortunately there areexcellent sources for this material
Trang 30No book can be a substitute for regular interaction with a trial methodologist duringboth the planning stages of a clinical investigation and its analysis I do not suggestpassive reliance on such consultations, but intend to facilitate disseminating knowledgefrom which true collaborations between clinicians and trialists will result Althoughmany clinicians think of bringing their final data to a statistician, a collaboration will
be most valuable during the design phase of a study when an experienced trialist mayprevent serious methodologic errors, help streamline a study, or suggest ways to avoidcostly mistakes
The wide availability of computers is a strong benefit for clinical researchers, butpresents some dangers Although computers facilitate efficient, accurate, and timelykeeping of data, modern software also permits or encourages researchers to pro-duce “statistical” reports without much attention to study design and without fullyunderstanding assumptions, methods, limitations, and pitfalls of the procedures beingemployed Sometimes a person who knows how to run procedure-oriented packages
on computerized data is called the “statistician,” even though he or she might be anovice at the basic theory underlying the analyses It then becomes possible to produce
a final report of a trial without the clinical investigator understanding the limitations
of analysis and without the analyst being conversant with the data What a weak chainthis is
The ideas in this book are intended to counteract these tendencies, not by being fashioned but by being rigorous Good design inhibits errors by involving a statisticalexpert in the study as a collaborator from the beginning Most aspects of the studywill improve as a result, including reliability, resource utilization, quality assurance,precision, and the scope of inference Good design can also simplify analyses byreducing bias and variability and removing the influence of complicating factors Inthis way number crunching becomes less important than sound statistical reasoning.The student of clinical trials should also understand that the field is growing andchanging in response to both biological and statistical developments A picture of goodmethodology today may be inadequate in the near future This is probably more true ofanalytic methods than design, where the fundamentals will change more slowly Analy-sis methods often will depend on new statistical developments or theory These in turndepend on (1) computing hardware, (2) reliable and accessible software, (3) trainingand re-training of trialists in the use of new methods, (4) acceptance of the procedure
old-by the statistical and biological communities, and (5) sufficient time for the innovations
to diffuse into practice
It is equally important to understand what changes or new concepts do not improvemethodology but are put forward in response to non-science issues or because of creep-ing regulation The best recent example of this is the increasing sacrifice of expertise infavor of objectivity in the structure and function of clinical trial monitoring (discussed
in Chapter 14) Such practices are sometimes as ill considered as they are well meaning,and may be promulgated by sponsors without peer review or national consensus.Good trial design requires a willingness to examine many alternatives within theconfines of reliably answering the basic biological question The most common errorsrelated to trial design are devoting insufficient resources or time to the study, rigidlyusing standard types of designs when better (e.g., more efficient) designs are available,
or undoing the benefits of a good design with a poorly planned (or executed) analysis
I hope that the reader of this book will come to understand where there is muchflexibility in the design and analysis of trials and where there is not
Trang 311.3 OTHER SOURCES OF KNOWLEDGE
The periodical literature related to clinical trials is large I have attempted to providecurrent useful references for accessing it in this book Aside from individual studyreports in many clinical journals, there are some periodicals strongly related to trials
One is Controlled Clinical Trials, which has been the official journal of the Society for
Clinical Trials (SCT) (mostly a U.S organization) The journal was begun in 1980 and
is devoted to trial methodology The SCT was founded in 1981 and its 1500 members
meet yearly In 2003 the SCT changed its official journal to Clinical Trials, the first
issue of which appeared in January 2004 This reincarnated journal should be an
excel-lent resource for trialists A second helpful periodical source is Statistics in Medicine,
which frequently has articles of interest to the trialist It began publication in 1982 and
is the official publication of the International Society for Clinical Biostatistics (mostly aEuropean organization) These two societies have begun joint meetings every few years.Many papers of importance to clinical trials and related statistical methods appear
in various other applied statistical and clinical journals Reviews of many methods
are published in Statistical Methods in Medical Research One journal of particular interest to drug development researchers is the Journal of Biopharmaceutical Statistics.
A useful general reference source is the journal Biostatistica, which contains abstracts
from diverse periodicals Statistical methodology for clinical trials appears in severaljournals The topic was reviewed with an extensive bibliography by Simon (1991)
A more extensive bibliography covering trials broadly has been given by Hawkins(1991)
In addition to journals there are a number of books and monographs dealing withclinical trials The text by Meinert (1986) is a practical view of the infrastructureand administrative supports necessary to perform quality trials, especially randomizedcontrolled trials Freidman, Furberg, and DeMets (1982) and Pocock (1996) also discussmany conceptual and practical issues in their excellent books, which do not requireextensive statistical background A nice encyclopedic reference regarding statisticalmethods in clinical trials is provided by Redmond and Colton (2001) There is arelatively short and highly readable methodology book by Silverman (1985), and asecond more issue oriented one (Silverman, 1998) with many examples Every trialistshould read the extensive work on placebos by Shapiro and Shapiro (1997)
In recent years many Web-based sources of information regarding clinical trialshave been developed The quality, content, and usefulness are highly variable and theuser must consider the source when browsing Resources of generally high quality that
I personally find useful are listed in Table 1.1 My list is probably not complete withregard to specialized needs, but it provides a good starting point
In the field of cancer trials, Buyse, Staquet, and Sylvester (1984) is an excellentsource, although now becoming slightly dated A contemporary view of cancer trials isgiven by Girling et al (2003) The book by Leventhal and Wittes (1988) is useful forits discussion of issues from a strong clinical orientation A very readable book withmany good examples is that by Green, Benedetti, and Crowley (2002) In the field
of AIDS, a useful source is Finkelstein and Schoenfeld (1995) The serious studentshould also be familiar with the classic papers by Peto et al (1977a, b)
Spilker (e.g., 1993) has written a large volume of material about clinical trials, much
of it oriented toward pharmaceutical research and not statistical methods Anotheruseful reference with an industry perspective is Wooding (1994) Data management
Trang 32TABLE 1.1 Some Web Resources for Clinical Trials Information
assert-statement.org A standard for the scientific and ethical review of
trials: a structured approach for ethics committees reviewing randomized controlled clinical trials cochrane.org The Cochrane Collaboration provides up-to-date
information about the effects of health care clinicaltrials.gov Provides information about federally and privately
supported clinical research.
consort-statement.org CONSORT statement: an evidence-based tool to
improve the quality of reports of randomized trials jameslindlibrary.org Evolution of fair tests of medical treatments;
examples from books and journal articles including key passages of text.
icmje.org Uniform requirements for manuscripts submitted to
biomedical journals gpp-guidelines.org Encourages responsible and ethical publication of
clinical trials sponsored by pharmaceutical companies
ncbi.nlm.nih.gov/entrez/query.fcgi PubMed: includes over 14 million citations for
biomedical articles back to the 1950’s mcclurenet.com/ICHefficacy.html ICH efficacy guidelines
controlled-trials.com Current controlled trials: provides access to peer
reviewed biomedical research
is an important subject for investigators, but falls outside the scope of this book Thesubject is probably made more complex by the fact that vastly more data are routinelycollected during developmental trials than are needed to meet the objectives Goodsources of knowledge concerning data management include the books by McFadden
(1998) and that edited by Rondel, Varley, and Webb (1993), and an issue of Controlled Clinical Trials (April 1995) devoted to data management Books on other relevant
topics will be mentioned in context later
Even in a very active program of clinical research, a relatively short exposure tothe practical side of clinical trials cannot illustrate all the important lessons This isbecause it may take years for any single clinical trial, and many such studies, to yieldall of their information useful for learning about methodology Even so, the student
of clinical trials will learn some lessons more quickly by being involved in an actualstudy, compared with simply studying theory In this book, I illustrate many conceptswith published trials In this way the reader can have the benefit of observing studiesfrom a long-term perspective, which would otherwise be difficult to acquire
1.3.1 Terminology
The terminology of clinical trials is not without its ambiguities A recent firm effort hasbeen made to standardize definitions in a dictionary devoted to clinical trial terminology(Meinert, 1996) Most of the terms within this book are used in a way consistentwith such definitions A notable exception is that I propose and employ explanatoryalternatives to the widely used, uninformative, inconsistent, and difficult-to-generalize
Trang 33“phase I, II, III, or IV” designations for clinical trials This topic is discussed inChapter 6.
Much of the terminology of clinical trials has been derived directly from drugdevelopment Because of the heavy use of clinical trials in the development of cytotoxicdrugs for the treatment of cancer between the 1960s and the 1990s, the terminology forthis setting has found its way inappropriately into other contexts Drug developmentterminology is often ambiguous and inappropriate for clinical trials performed in manyother areas, and is even unsuitable for many new cancer therapies that do not actthrough a direct cytotoxic mechanism For this reason I have not employed this outdatedterminology in this book and have used descriptive alternatives (Section 6.3.2)
1.3.2 Review of Notation and Terminology Is Helpful
There is no escaping the need for mathematical formalism in the study of clinicaltrials It would be unreasonable, a priori, to expect mathematics to be as useful as
it is in describing nature (Wigner, 1959) Nevertheless, it is, and the mathematics ofprobability is the particular area most helpful for clinical trials Galileo said:
The book of the universe is written in mathematical language, without which one wanders
in vain through a dark labyrinth.
Statisticians light their dark labyrinth using abstract symbols (e.g., Greek letters) as
a shorthand for important mathematical quantities and concepts I will also use thesesymbols when appropriate in this book, because many ideas are troublesome to explainwithout good notation
However, because this book is not oriented primarily toward statistical theory, theuse of symbols will be minimal and tolerable, even to nonmathematical readers Areview and explanation of common usage of symbols consistent with the clinical trialsliterature is given in Appendix B Because some statistical terms may be unfamiliar tosome readers, definitions and examples are also listed in that chapter Abbreviationsused in the book are also explained there
This book does not and cannot provide the technical statistical background that isneeded to understand clinical trial design and analysis thoroughly As stated above,much of this knowledge is assumed to be present Help is available in the form ofpractical and readable references Examples are the books by Armitage and Berry(1994) and Marubini and Valsecchi (1995) More concise summaries are given byCampbell and Machin (1990) and Everitt (1989) A comprehensive reference with
good entries to the literature is the Encyclopedia of Biostatistics (Armitage and Colton,
1998) More specialized references will be mentioned later
1.4 EXAMPLES, DATA, AND PROGRAMS
It is not possible to learn all the important lessons about clinical trials from classroominstruction or reading, nor is it possible for every student to be involved with actualtrials as part of a structured course This problem is most correctable for topics related
to the analysis of trial results, where real data can be provided For some examplesand problems used in this book, the data are provided through the author’s Web site
Trang 34(www.cancerbiostats.onc.jhmi.edu/) Throughout the book, I have made a concertedeffort to provide examples of trials that are instructive but small, so as to be digestible
by the student Computerized data files and programs to read and analyze them areprovided on the Web site It also contains some sample size and related programsthat are helpful for design calculations More powerful sample size (and other) designsoftware that is available commercially is discussed in Chapter 11
Many, but not all, tables, figures, and equations in the text have been programmed
in Mathematica, Version 5 (Wolfram, 2003) The computer code related to the book isavailable from the author’s Web site Mathematica, which is required to use the relevantprograms, is commercially available The stand-alone programs mentioned above andMathematica code are made available for instructional purposes without warranty ofany kind—the user assumes responsibility for all results
1.5 SUMMARY
The premise of this book is that well-designed experimental research is a necessarybasis for therapeutic development and clinical care decisions The purpose of thisbook is to address issues in the methodology of clinical trials in a format accessible tointerested statistical and clinical scientists The audience is intended to be practicingclinicians, statisticians, trialists, and others with a need for understanding good clin-ical research methodology The reader familiar with clinical trials will notice a fewsubstantive differences from usual discussions, including the use of descriptive termsfor types of trials and an even-handed treatment of different statistical perspectives.Examples from the clinical trials literature are used, and data and computer programsfor some topics are available A review of essential notation and terminology is alsoprovided
Trang 35be claimed that the entire history of therapeutics up to that point was essentially onlythe history of the placebo effect (Shapiro and Shapiro, 1997) However, when scientistsshowed that diseases like pellagra and diabetes could have their effects relieved withmedicinals, belief in treatment began to return Following the discovery of penicillinand sulfanilamide in the twentieth century, the period of nihilism ended (Thomas, 1977;Coleman, 1987).
More recently discovery of effective drugs for the treatment of cancer, cardiovasculardisease, infections, and mental illness as well as the crafting of vaccines and otherpreventive measures have demonstrated the value of therapeutics There is economicevidence and opinion to support the idea that the strong overall economic status of theUnited States is substantially due to improved health of the population (Funding First,2000), itself dependent on effective public health and therapeutic interventions Clinicalinvestigation methods are important in the search for effective prevention agents andtreatments, sorting out the benefits of competing therapies, and establishing optimumtreatment combinations and schedules
Experimental design and analysis have become essential because of the greaterdetail in modern biological theories and the complexities in treatments of disease Theclinician is usually interested in small, but biologically important, treatment effectsthat can be obscured by uncontrolled natural variation and bias in nonrigorous studies
Clinical Trials: A Methodologic Perspective, 2E, by S Piantadosi
Copyright 2005 John Wiley & Sons, Inc.
9
Trang 36This places well-performed clinical trials at the very center of clinical research today,although the interest in small effect sizes also creates problems for other aspects ofclinical investigation (Ahrens, 1992).
Other contemporary pressures also encourage the application of rigorous clinicaltrials Societal expectations to relieve suffering through medical progress, governmentalregulation of prescription drugs and devices, and the economics of pharmaceuticaldevelopment all encourage or demand efficient and valid study design Nearly all goodclinical trials have basic biological, public health, and commercial value, encouraginginvestigators to design studies that yield timely and reliable results
In the early twenty-first century the pendulum of nihilism has swung strongly in theopposite direction Today there is belief in the therapeutic efficacy of many treatments.Traditional medicine, complimentary, alternative, fringe, and other methods abound,with their own advocates and practitioners Many patients put their confidence inuntested or inadequately tested treatments Even in disease areas where therapies areevaluated rigorously, many patients assume treatments are effective, or at least worththe risk, or they would not be under investigation Other patients are simply willing totake a chance that a new treatment will work, especially when the side effects appear
to be minimal
To a rigorous modern clinical investigator, these comprise opportunities to servethe needs of patients and practitioners by providing the most reliable evidence abouttreatment effects and risks These circumstances often provide pressure to use clinicaltrials However, the same forces can create incentives to bypass rigorous evaluationmethods because strong beliefs of efficacy can arise from unreliable data, as has beenthe case historically Whether contemporary circumstances encourage or discourageclinical trials depends largely on mindset and values
A trialist must understand two different modes of thinking that support the ence—clinical and statistical They both underlie the re-emergence of therapeutics as amodern science Each method of reasoning arose independently and must be combinedskillfully if they are to serve therapeutic questions effectively
sci-2.1.1 Clinical Reasoning Is Based on the Case History
The word clinical is derived from the Greek kline, which means bed In modern usage, clinical not only refers to the bedside but pertains more generally to the care
of human patients The quantum unit of clinical reasoning is the case history, and theprimary focus of clinical inference is the individual patient Before the widespreaduse of experimental trials, clinical methods of generalizing from the individual tothe population were informal The concepts of person-to-person variability and itssources were also described informally Medical experience and judgment was not,and probably cannot be, captured in a set of rules Instead, it is a form of “tacitknowledge” (Polanyi, 1958), and is very concrete
New and potentially useful clinical observations are made against this background
of reliable experience Following such observation, many advances have been made byincremental improvement of existing ideas This process explains much of the progressmade in medicine and biology up to the twentieth century Incremental improvement is
a reliable but slow method that can optimize many complex processes For example, thewriting of this book proceeded largely by slightly improving earlier drafts, especiallytrue for the second edition However, there was a foundation of design that greatly
Trang 37facilitated the entire process Clinical trials can provide a similar foundation of designfor clinical inference, greatly amplifying the benefits of careful observation.
There often remains discomfort in clinical settings over the extent to which lation-based estimates (i.e., those from a clinical trial) pertain to any individual, espe-cially a new patient outside the study This is not so much a problem interpreting theresults of a clinical trial as a difficulty trying to use results to select the best treatmentfor a new individual There is no formal way to accomplish this generalization in apurely clinical framework It depends on judgment, which itself depends on experience.However, clinical experience historically has been summarized in nonstatistical ways
popu-A stylized example of clinical reasoning, and a rich microcosm of issues, can beseen in the following case history, transmitted by Francis Galton (1899):
The season of strawberries is at hand, but doctors are full of fads, and for the most part forbid them to the gouty Let me put heart to those unfortunate persons to withstand a cruel medical tyranny by quoting the experience of the great Linnæus It will be found
in the biographical notes, written by himself in excellent dog-latin, and published in the life of him by Dr H Stoever, translated from German into English by Joseph Trapp,
1794 Linnæus describes the goutiness of his constitution in p 416 (cf p 415) and says
that in 1750 he was attacked so severely by siatica that he could hardly make his way home The pain kept him awake during a whole week He asked for opium, but a friend dissuaded it Then his wife suggested “Won’t you eat strawberries?” It was the season for
them Linnæus, in the spirit of experimental philosopher, replied, “tentabo — I will make
the trial.” He did so, and quickly fell into a sweet sleep that lasted two hours, and when
he awoke the pain had sensibly diminished He asked whether any strawberries were left: there were some, and he ate them all Then he slept right away till morning On the next day, he devoured as many strawberries as he could, and on the subsequent morning the pain was wholly gone, and he was able to leave his bed Gouty pains returned at the same date in the next year, but were again wholly driven off by the delicious fruit; similarly in the third year Linnæus died soon after, so the experiment ceased.
What lucrative schemes are suggested by this narrative Why should gouty persons drink nasty waters, at stuffy foreign spas, when strawberry gardens abound in England? Let enthusiastic young doctors throw heart and soul into the new system Let a company be run to build a curhaus in Kent, and let them offer me board and lodging gratis in return for my valuable hints.
The pedigree of the story may have been more influential than the evidence it vides It has been viewed both as quackery and as legitimate (Porter and Rousseau,1998), but as a trialist and occasional sufferer of gout, I find the story both quaint andenlightening with regard to a clinical mindset Note especially how the terms “trial”and “experiment” were used, and the tone of determinism
pro-Despite its successes clinical reasoning by itself has no way to deal formally with
a fundamental problem regarding treatment inefficacy Simply stated, that problem is
“why do ineffective treatments frequently appear to be effective?” The answer to thisquestion may include some of the following reasons:
• The disease has finished its natural course
• There is a natural exacerbation–remission cycle
• Spontaneous cure has occurred
• The placebo effect
Trang 38• There is a psychosomatic cause and, hence, a cure by suggestion
• The diagnosis is incorrect
• Relief of symptoms has been confused with cure
• Distortions of fact by the practitioner or patient
inef-2.1.2 Statistical Reasoning Emphasizes Inference Based
on Designed Data Production
The word statistics is derived from the Greek statis and statista, which mean state The exact origin of the modern usage of the term statistics is obscured by the fact
that the word was used mostly in a political context to describe territory, populations,trade, industry, and related characteristics of countries from the 1500s until about 1850
A brief review of this history was given by Kendall, who stated that scholars began
using data in a reasoned way around 1660 (Kendall, 1960) The word statistik was used
in 1748 to describe a particular body of analytic knowledge by the German scholar
Gottfried Achenwall (1719–1772) in Vorbereitung zur Staatswissenschaft (Achenwall,
1748; Hankins, 1930) The context seems to indicate that the word was already used
in the way we mean it now, but some later writers suggest that he originated it (Fang,1972; Liliencron, 1967) Porter (1986) gives the date for the use of the German term
statistik as 1749.
Statistics is a highly developed information science It encompasses the formal study
of the inferential process, especially the planning and analysis of experiments, surveys,
or observational studies It became a distinct field of study only in the twentieth century(Stigler, 1986) Although based largely on probability theory, statistics is not, strictlyspeaking, a branch of mathematics Even though the same methods of axioms, formaldeductive reasoning, and logical proof are used in both statistics and mathematics, thefields are distinct in origin, theory, practice, and application Barnett (1982) discussesvarious views of statistics, eventually defining it as:
the study of how information should be employed to reflect on, and give guidance for action in, a practical situation involving uncertainty.
Making reasonable, accurate, and reliable inferences from data in the presence ofuncertainty is an important and far-reaching intellectual skill It is not merely a col-lection of ad hoc tricks and techniques, an unfortunate view occasionally held byclinicians and some grant reviewers Statistics is a way of thinking or an approach toeveryday problems that relies heavily on designed data production An essential impact
of statistical thought is that it minimizes the chance of drawing incorrect conclusionsfrom either good or bad data
Trang 39Modern statistical theory is the product of extensive intellectual development in theearly to middle twentieth century and has found application in most areas of science.
It is not obvious in advance that such a theory should be applicable across a largenumber of disciplines That is one of the most remarkable aspects of statistical theory.Despite the wide applicability of statistical reasoning, it remains an area of substantialignorance for many scientists
Statistical reasoning is characterized by the following general methods, in roughlythis order:
1 Establish an objective framework for conducting an investigation
2 Place data and theory on an equal scientific footing
3 Employ designed data production through experimentation
4 Quantify the influence of chance on outcomes
5 Estimate systematic and random effects
6 Combine theory and data using formal methods to make inferences
Reasoning using these tools enhances validity and permits efficient use of information,time, and resources
Perhaps because it embodies a sufficient degree of abstraction but remains grounded
by practical questions, statistics has been very broadly successful Through its ematical connections, statistical reasoning permits or encourages abstraction that isuseful for solving the problem at hand and other similar ones This universality is
math-a gremath-at math-advmath-antmath-age of math-abstrmath-action In math-addition math-abstrmath-action cmath-an often clmath-arify outcomes,measurements, or analyses that might otherwise be poorly defined Finally abstraction
is a vehicle for creativity
2.1.3 Clinical and Statistical Reasoning Converge in Research
Because of their different origins and purposes, clinical and statistical reasoning could
be viewed as fundamentally incompatible But the force that combines these differenttypes of reasoning is research A clinical researcher is someone who investigates formalhypotheses arising from work in the clinic (Frei, 1982; Frei and Freireich, 1993) Thisrequires two interdependent tasks that statistics does well: generalizing observationsfrom few to many, and combining empirical and theory-based knowledge
In the science of clinical research, empirical knowledge comes from experience,observation, and data Theory-based knowledge arises from either established biology
or hypothesis In statistics, the empirical knowledge comes from data or observations,while the theory-based knowledge is that of probability and determinism, formalized
in mathematical models Models specifically, and statistics in general, are the mostefficient and useful way to combine theory and observation
This mixture of reasoning explains both the successful application of statisticsbroadly and the difficulty that some clinicians have in understanding and applyingstatistical modes of thought In most purely clinical tasks, as indicated above, there isrelatively little need for statistical modes of reasoning The best use and interpretation
of diagnostic tests is one interesting exception Clinical research, in contrast, demandscritical and quantitative views of research designs and data The mixture of modes
of reasoning provides a solution to the inefficacy problem outlined in Section 2.1.1
Trang 40To perform, report, and interpret research studies reliably, clinical modes of reasoningmust be reformed by statistical ideas Carter, Scheaffer, and Marks (1986) focused onthis point appropriately when they said:
Statistics is unique among academic disciplines in that statistical thought is needed at every stage of virtually all research investigations, including planning the study, selecting the sample, managing the data, and interpreting the results.
Failure to master statistical concepts can lead to numerous and important errorsand biases in medical research, a compendium of which is given by Andersen (1990).Coincident with this need for statistical knowledge in the clinic, it is necessary forthe clinical trials statistician to master fundamental biological and clinical conceptsrelevant to the disease under study Failure to accomplish this can also lead to seriousmethodological and inferential errors A clinical researcher must consult the statisticalexpert early enough in the conceptual development of the experiment to improve thestudy The clinical researcher who involves a statistician only in the “analysis” of datafrom a trial can expect a substantially inferior product overall
2.2 DEFINING CLINICAL TRIALS FORMALLY
2.2.1 Mixing of Clinical and Statistical Reasoning Is Recent
The historical development of clinical trials has depended mostly on biological andmedical advances, as opposed to applied mathematical or statistical developments Abroad survey of mathematical advances in the biological and medical sciences supportsthis interpretation (Lancaster, 1994) For example, the experimental method was known
to the Greeks, especially Strato of Lampsacus (c 250 BCE) (Magner, 2002) TheGreek anatomists, Herophilus and Erasistratis in the third century BCE, demonstrated
by vivisection of prisoners that loss of movement or sensation occurred when nerveswere severed Such studies were not perpetuated, but it would be two millennia beforeadequate explanations for the observations would be formulated (F.R Wilson, 1998;Staden, 1992)
There was considerable opposition to the application of statistics in medicine, cially in the late eighteenth century and early nineteenth century when methods werefirst being developed The numerical method, as it was called, was proposed and devel-oped in the early nineteenth century and has become most frequently associated withPierre Charles Alexander Louis His best-known and most controversial study was pub-lished in 1828, and examined the effects of bloodletting as treatment for pneumonia(see Louis, 1835) Although the results did not clearly favor or disfavor bloodletting,his work became controversial because it appeared to challenge conventional practice
espe-on the basis of numerical results The study was criticized, in part, because the ual cases were heterogeneous and the number of patients was relatively small Therewas even a claim in 1836 by d’Amador that the use of probability in therapeutics wasantiscientific (d’Amador, 1836)
individ-Opposition to the application of mathematical methods in biology also came fromClaude Bernard (1865) His argument was also based partly on individual heterogene-ity Averages, he felt, were as obscuring as they were illuminating Gavarret gave aformal specification of the principles of medical statistics in 1840 (Gavarret, 1840)