Already popular in the analysis of medical device trials, adaptive Bayesian designs are increasingly being used in drug development for a wide variety of diseases and conditions, from Alzheimer’s disease and multiple sclerosis to obesity, diabetes, hepatitis C, and HIV. Written by leading pioneers of Bayesian clinical trial designs, Bayesian Adaptive Methods for Clinical Trials explores the growing role of Bayesian thinking in the rapidly changing world of clinical trial analysis. The book first summarizes the current state of clinical trial design and analysis and introduces the main ideas and potential benefits of a Bayesian alternative. It then gives an overview of basic Bayesian methodological and computational tools needed for Bayesian clinical trials. With a focus on Bayesian designs that achieve good power and Type I error, the next chapters present Bayesian tools useful in early (Phase I) and middle (Phase II) clinical trials as well as two recent Bayesian adaptive Phase II studies: the BATTLE and ISPY-2 trials. In the following chapter on late (Phase III) studies, the authors emphasize modern adaptive methods and seamless Phase II–III trials for maximizing information usage and minimizing trial duration. They also describe a case study of a recently approved medical device to treat atrial fibrillation. The concluding chapter covers key special topics, such as the proper use of historical data, equivalence studies, and subgroup analysis. For readers involved in clinical trials research, this book significantly updates and expands their statistical toolkits. The authors provide many detailed examples drawing on real data sets. The R and WinBUGS codes used throughout are available on supporting websites. Scott Berry talks about the book on the CRC Press YouTube Channel.
Author(s): Scott M. Berry, Bradley P. Carlin, J. Jack Lee, Peter Muller
Edition: 1
Publisher: CRC Press
Year: 2010
Language: English
Pages: 307
Tags: Медицинские дисциплины;Социальная медицина и медико-биологическая статистика;
Cover......Page 1
Bayesian
Adaptive Methods
for Clinical Trials......Page 2
Contents......Page 8
Foreword......Page 12
Preface......Page 14
1.1 Introduction......Page 17
1.2 Comparisons between Bayesian and frequentist approaches......Page 20
1.3 Adaptivity in clinical trials......Page 22
1.4.1 The fully Bayesian approach......Page 24
1.4.2 Bayes as a frequentist tool......Page 26
1.4.3 Examples of the Bayesian approach to drug and medical device development......Page 28
2.1 Introduction to Bayes' Theorem......Page 34
2.2.1 Point estimation......Page 41
2.2.2 Interval estimation......Page 42
2.2.3 Hypothesis testing and model choice......Page 44
2.2.4 Prediction......Page 49
2.2.5 Effect of the prior: sensitivity analysis......Page 52
2.2.6 Role of randomization......Page 53
2.2.7 Handling multiplicities......Page 55
2.3 Bayesian computation......Page 57
2.3.1 The Gibbs sampler......Page 59
2.3.2 The Metropolis-Hastings algorithm......Page 60
2.3.3 Convergence diagnosis......Page 63
2.3.4 Variance estimation......Page 64
2.4 Hierarchical modeling and metaanalysis......Page 66
2.5 Principles of Bayesian clinical trial design......Page 78
2.5.1 Bayesian predictive probability methods......Page 79
2.5.2 Bayesian indifference zone methods......Page 81
2.5.3 Prior determination......Page 83
2.5.4 Operating characteristics......Page 85
2.5.5 Incorporating costs......Page 93
2.5.6 Delayed response......Page 96
2.5.7 Noncompliance and causal modeling......Page 97
2.6 Appendix: R Macros......Page 101
3. Phase I studies......Page 102
3.1.1 Traditional 3+3 design......Page 103
3.1.2 Pharmacologically guided dose escalation......Page 106
3.1.5 Summary of rule-based designs......Page 107
3.2 Model-based designs for determining the MTD......Page 108
3.2.1 Continual reassessment method (CRM)......Page 109
3.2.2 Escalation with overdose control (EWOC)......Page 117
3.2.3 Time-to-event (TITE) monitoring......Page 120
3.2.4 Toxicity intervals......Page 124
3.2.5 Ordinal toxicity intervals......Page 128
3.3 Efficacy versus toxicity......Page 131
3.3.2 Joint probability model for efficacy and toxicity......Page 132
3.3.4 Efficacy-toxicity trade-off contours......Page 133
3.4 Combination therapy......Page 136
3.4.1 Basic Gumbel model......Page 137
3.4.2 Bivariate CRM......Page 141
3.4.3 Combination therapy with bivariate response......Page 142
3.4.4 Dose escalation with two agents......Page 144
3.5 Appendix: R Macros......Page 149
4.1 Standard designs......Page 151
4.1.1 Phase IIA designs......Page 152
4.1.2 Phase IIB designs......Page 154
4.2 Predictive probability......Page 156
4.2.1 Definition and basic calculations for binary data......Page 157
4.2.2 Derivation of the predictive process design......Page 160
4.3.1 Binary stopping for futility and efficacy......Page 164
4.3.2 Binary stopping for futility, efficacy, and toxicity......Page 165
4.3.3 Monitoring event times......Page 168
4.4.1 Principles of adaptive randomization......Page 169
4.4.2 Dose ranging and optimal biologic dosing......Page 177
4.4.3 Adaptive randomization in dose finding......Page 181
4.4.4 Outcome adaptive randomization with delayed survival response......Page 182
4.5 Hierarchical models for phase II designs......Page 187
4.6.1 Utility functions and their specification......Page 190
4.6.2 Screening designs for drug development......Page 193
4.7.1 The BATTLE trial......Page 197
4.7.2 The I-SPY 2 trial......Page 203
4.8 Appendix: R Macros......Page 205
5.1 Introduction to confirmatory studies......Page 206
5.2 Bayesian adaptive confirmatory trials......Page 208
5.2.1 Adaptive sample size using posterior probabilities......Page 209
5.2.2 Futility analyses using predictive probabilities......Page 213
5.2.3 Handling delayed outcomes......Page 217
5.3 Arm dropping......Page 221
5.4 Modeling and prediction......Page 224
5.5 Prior distributions and the paradigm clash......Page 231
5.6 Phase III cancer trials......Page 234
5.7 Phase II/III seamless trials......Page 241
5.7.1 Example phase II/III trial......Page 243
5.7.2 Adaptive design......Page 244
5.7.3 Statistical modeling......Page 245
5.7.4 Calculation......Page 246
5.7.5 Simulations......Page 248
5.8 Case study: Ablation device to treat atrial fibrillation......Page 254
5.9 Appendix: R Macros......Page 260
6.1 Incorporating historical data......Page 261
6.1.1 Standard hierarchical models......Page 262
6.1.2 Hierarchical power prior models......Page 264
6.2 Equivalence studies......Page 272
6.2.1 Statistical issues in bioequivalence......Page 273
6.2.2 Binomial response design......Page 275
6.2.3 2 × 2 crossover design......Page 277
6.3 Multiplicity......Page 280
6.3.1 Assessing drug safety......Page 281
6.3.2 Multiplicities and false discovery rate (FDR)......Page 287
6.4.1 Bayesian approach......Page 288
6.4.2 Bayesian decision theoretic approach......Page 289
6.5 Appendix: R Macros......Page 292
References......Page 293
