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Summary
Driven by modern computing capabilities, there has been a rapid increase of interest in Bayesian methods for the analysis of clinical trial data. This book provides an introduction to Bayesian adaptive methods for the design and analysis of clinical trials. It presents background material on Bayesian inference and covers clinical trials in Phases I, II, and III. The methods are illustrated with many detailed examples and case studies using real data obtained by the authors in their extensive work. The examples are implemented using R and WinBUGS software, which is made available on a supporting website.
Table of Contents
| Foreword | p. xi |
| Preface | p. xiii |
| Statistical approaches for clinical trials | p. 1 |
| Introduction | p. 1 |
| Comparisons between Bayesian and frequentist approaches | p. 4 |
| Adaptivity in clinical trials | p. 6 |
| Features and use of the Bayesian adaptive approach | p. 8 |
| The fully Bayesian approach | p. 8 |
| Bayes as a frequentist tool | p. 10 |
| Examples of the Bayesian approach to drug and medical device development | p. 12 |
| Basics of Bayesian inference | p. 19 |
| Introduction to Bayes' Theorem | p. 19 |
| Bayesian inference | p. 26 |
| Point estimation | p. 26 |
| Interval estimation | p. 27 |
| Hypothesis testing and model choice | p. 29 |
| Prediction | p. 34 |
| Effect of the prior: sensitivity analysis | p. 37 |
| Role of randomization | p. 38 |
| Handling multiplicities | p. 40 |
| Bayesian computation | p. 42 |
| The Gibbs sampler | p. 44 |
| The Metropolis-Hastings algorithm | p. 45 |
| Convergence diagnosis | p. 48 |
| Variance estimation | p. 49 |
| Hierarchical modeling and metaanalysis | p. 51 |
| Principles of Bayesian clinical trial design | p. 63 |
| Bayesian predictive probability methods | p. 64 |
| Bayesian indifference zone methods | p. 66 |
| Prior determination | p. 68 |
| Operating characteristics | p. 70 |
| Incorporating costs | p. 78 |
| Delayed response | p. 81 |
| Noncompliance and causal modeling | p. 82 |
| Appendix: R Macros | p. 86 |
| Phase I studies | p. 87 |
| Rule-based designs for determining the MTD | p. 88 |
| Traditional 3+3 design | p. 88 |
| Pharmacologically guided dose escalation | p. 91 |
| Accelerated titration designs | p. 92 |
| Other rule-based designs | p. 92 |
| Summary of rule-based designs | p. 92 |
| Model-based designs for determining the MTD | p. 93 |
| Continual reassessment method (CRM) | p. 94 |
| Escalation with overdose control (EWOC) | p. 102 |
| Time-to-event (TITE) monitoring | p. 105 |
| Toxicity intervals | p. 109 |
| Ordinal toxicity intervals | p. 113 |
| Efficacy versus toxicity | p. 116 |
| Trial parameters | p. 117 |
| Joint probability model for efficacy and toxicity | p. 117 |
| Defining the acceptable dose levels | p. 118 |
| Efficacy-toxicity trade-off contours | p. 118 |
| Combination therapy | p. 121 |
| Basic Gumbel model | p. 122 |
| Bivariate CRM | p. 126 |
| Combination therapy with bivariate response | p. 127 |
| Dose escalation with two agents | p. 129 |
| Appendix: R Macros | p. 134 |
| Phase II studies | p. 137 |
| Standard designs | p. 137 |
| Phase IIA designs | p. 138 |
| Phase IIB designs | p. 140 |
| Limitations of traditional frequentist designs | p. 142 |
| Predictive probability | p. 142 |
| Definition and basic calculations for binary data | p. 143 |
| Derivation of the predictive process design | p. 146 |
| Sequential stopping | p. 150 |
| Binary stopping for futility and efficacy | p. 150 |
| Binary stopping for futility, efficacy, and toxicity | p. 151 |
| Monitoring event times | p. 154 |
| Adaptive randomization and dose allocation | p. 155 |
| Principles of adaptive randomization | p. 155 |
| Dose ranging and optimal biologic dosing | p. 163 |
| Adaptive randomization in dose finding | p. 167 |
| Outcome adaptive randomization with delayed survival response | p. 168 |
| Hierarchical models for phase II designs | p. 173 |
| Decision theoretic designs | p. 176 |
| Utility functions and their specification | p. 176 |
| Screening designs for drug development | p. 179 |
| Case studies in phase II adaptive design | p. 183 |
| The Battle trial | p. 183 |
| The I-SPY 2 trial | p. 189 |
| Appendix: R Macros | p. 191 |
| Phase III studies | p. 193 |
| Introduction to confirmatory studies | p. 193 |
| Bayesian adaptive confirmatory trials | p. 195 |
| Adaptive sample size using posterior probabilities | p. 196 |
| Futility analyses using predictive probabilities | p. 200 |
| Handling delayed outcomes | p. 204 |
| Arm dropping | p. 208 |
| Modeling and prediction | p. 211 |
| Prior distributions and the paradigm clash | p. 218 |
| Phase III cancer trials | p. 221 |
| Phase II/III seamless trials | p. 228 |
| Example phase II/III trial | p. 230 |
| Adaptive design | p. 231 |
| Statistical modeling | p. 232 |
| Calculation | p. 233 |
| Simulations | p. 235 |
| Case study: Ablation device to treat atrial fibrillation | p. 241 |
| Appendix: R Macros | p. 247 |
| Special topics | p. 249 |
| Incorporating historical data | p. 249 |
| Standard hierarchical models | p. 250 |
| Hierarchical power prior models | p. 252 |
| Equivalence studies | p. 260 |
| Statistical issues in bioequivalence | p. 261 |
| Binomial response design | p. 263 |
| 2 x 2 crossover design | p. 265 |
| Multiplicity | p. 268 |
| Assessing drug safety | p. 269 |
| Multiplicities and false discovery rate (FDR) | p. 275 |
| Subgroup analysis | p. 276 |
| Bayesian approach | p. 276 |
| Bayesian decision theoretic approach | p. 277 |
| Appendix: R Macros | p. 280 |
| References | p. 281 |
| Author index | p. 297 |
| Index | p. 303 |
| Table of Contents provided by Ingram. All Rights Reserved. |
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