Randomization in Clinical Trials : Theory and Practice.

Cover -- Title Page -- Copyright -- Contents -- Preface -- Second edition -- First edition -- Chapter 1 Randomization and the Clinical Trial -- 1.1 Introduction -- 1.2 Causation and Association -- 1.3 Randomized Clinical Trials -- 1.4 Ethics of Randomization -- 1.5 Problems -- 1.6 References -- Chapter 2 Issues in the Design of Clinical Trials -- 2.1 Introduction -- 2.2 Study Outcomes -- 2.3 Sources of Bias -- 2.3.1 Selection and ascertainment bias -- 2.3.2 Statistical analysis philosophy -- 2.3.3 Losses to follow-up and noncompliance -- 2.3.4 Covariates -- 2.4 Experimental Design -- 2.5 Recruitment and Follow-Up -- 2.6 Determining the Number of Randomized Subjects -- 2.6.1 Development of the main formula -- 2.6.2 Example -- 2.6.3 Survival trials -- 2.6.4 Adjustment for noncompliance -- 2.6.5 Additional considerations -- 2.7 Problems -- 2.8 References -- Chapter 3 Randomization for Balancing Treatment Assignments -- 3.1 Introduction -- 3.2 Complete Randomization -- 3.3 Forced Balance Procedures -- 3.3.1 Random allocation rule -- 3.3.2 Truncated binomial design -- 3.3.3 Hadamard randomization -- 3.3.4 Maximal procedure -- 3.4 Forced Balance Randomization Within Blocks -- 3.4.1 Permuted block design -- 3.4.2 Random block design -- 3.5 Efron's Biased Coin Design -- 3.6 Other Biased Coin Designs and Generalizations -- 3.7 Wei's Urn Design -- 3.8 Other urn Models and Generalizations -- 3.9 Comparison of Balancing Properties -- 3.10 Restricted Randomization for Unbalanced Allocation -- 3.11 K &gt -- 2 Treatments -- 3.12 Problems -- 3.13 References -- 3.14 Appendix -- Chapter 4 The Effects of Unobserved Covariates -- 4.1 Introduction -- 4.2 A Bound on the Probability of a Covariate Imbalance -- 4.3 Simulation Results -- 4.4 Accidental Bias -- 4.5 Maximum Eigenvalue of ΣT -- 4.6 Accidental Bias for Biased Coin Designs -- 4.7 Chronological Bias.

4.8 Problems -- 4.9 References -- 4.10 Appendix -- Chapter 5 Selection Bias -- 5.1 Introduction -- 5.2 The Blackwell-Hodges Model -- 5.3 Predictability of a Randomization Sequence -- 5.4 Selection Bias for the Random Allocation Rule and Truncated Binomial Design -- 5.5 Selection Bias in a Permuted Block Design -- 5.5.1 Permuted blocks using the random allocation rule -- 5.5.2 Permuted blocks with truncated binomial randomization -- 5.5.3 Random block design -- 5.5.4 Conclusions -- 5.6 Selection Bias for Other Restricted Randomization Procedures -- 5.6.1 Efron's biased coin design -- 5.6.2 Wei's urn design -- 5.6.3 Smith's design -- 5.7 Simulation Results -- 5.8 Controlling and Testing for Selection Bias in Practice -- 5.9 Problems -- 5.10 References -- 5.11 Appendix -- Chapter 6 Randomization as a Basis for Inference -- 6.1 Introduction -- 6.2 The Population Model -- 6.3 The Randomization Model -- 6.4 Randomization Tests -- 6.5 Linear Rank Tests -- 6.6 Variance of the Linear Rank Test -- 6.7 Optimal Rank Scores -- 6.8 Exact and Large-Sample Randomization Tests -- 6.8.1 Computation of exact tests -- 6.8.2 Large sample randomization tests -- 6.9 Monte Carlo Re-Randomization Tests -- 6.9.1 Unconditional tests -- 6.9.2 Example -- 6.9.3 Conditional tests -- 6.10 Preservation of Error Rates -- 6.11 Regression Modeling -- 6.12 Analyses with Missing Data -- 6.13 Sample Size Considerations for Random Sample Fractions -- 6.14 Group Sequential Monitoring -- 6.14.1 Establishing a stopping boundary -- 6.14.2 Information fraction -- 6.15 Problems -- 6.16 References -- 6.17 Appendix A -- 6.18 Appendix B -- Chapter 7 Stratification -- 7.1 Introduction -- 7.2 Stratified Randomization -- 7.3 Is Stratification Necessary? -- 7.4 Treatment Imbalances in Stratified Trials -- 7.5 Stratified Analysis Using Randomization Tests.

7.6 Efficiency of Stratified Randomization in a Stratified Analysis -- 7.7 Conclusions -- 7.8 Problems -- 7.9 References -- Chapter 8 Restricted Randomization in Practice -- 8.1 Introduction -- 8.2 Stratification -- 8.3 Characteristics of Randomization Procedures -- 8.3.1 Consideration of selection bias -- 8.3.2 Implications for analysis -- 8.4 Selecting a Randomization Procedure -- 8.4.1 Choosing parameter values -- 8.4.2 Comparing procedures -- 8.4.3 Conclusions -- 8.5 Generation of Sequences -- 8.6 Implementation -- 8.6.1 Packaging and labeling -- 8.6.2 The actual randomization -- 8.7 Special Situations -- 8.8 Some Examples -- 8.8.1 The optic neuritis treatment trial -- 8.8.2 Vesnarinone in congestive heart failure -- 8.8.3 The diabetes control and complications trial -- 8.8.4 Captopril in diabetic nephropathy -- 8.8.5 The diabetes prevention program -- 8.8.6 Scleral buckling versus primary vitrectomy in retinal detachment (The SPR Study) -- 8.9 Problems -- 8.10 References -- Chapter 9 Covariate-Adaptive Randomization -- 9.1 Early Work -- 9.1.1 Zelen's rule -- 9.1.2 The Pocock-Simon procedure -- 9.1.3 Example: Adjuvant chemotherapy for locally invasive bladder cancer -- 9.1.4 Wei's marginal urn design -- 9.1.5 Is marginal balance sufficient? -- 9.1.6 Is randomization necessary? -- 9.2 More Recent Covariate-Adaptive Randomization Procedures -- 9.2.1 Balancing within strata -- 9.2.2 Balancing with respect to continuous covariates -- 9.3 Optimal Design Based on a Linear Model -- 9.4 The Trade-Off Among Balance, Efficiency, and Ethics -- 9.5 Inference for Covariate-Adaptive Randomization -- 9.5.1 Model-based inference -- 9.5.2 Randomization-based inference -- 9.6 Conclusions -- 9.7 Problems -- 9.8 References -- Chapter 10 Response-Adaptive Randomization -- 10.1 Introduction -- 10.2 Historical Notes -- 10.2.1 Roots in bandit problems.

10.2.2 Roots in sequential stopping problems -- 10.2.3 Roots in randomization -- 10.3 Optimal Allocation -- 10.4 Response-Adaptive Randomization to Target R* -- 10.4.1 Sequential maximum likelihood procedure -- 10.4.2 Doubly adaptive biased coin design -- 10.4.3 Example -- 10.4.4 Efficient randomized-adaptive design -- 10.5 Urn Models -- 10.5.1 The generalized Friedman's urn model -- 10.5.2 The randomized play-the-winner rule -- 10.5.3 Designs to target any allocation -- 10.5.4 Ternary urn models -- 10.5.5 Klein urn -- 10.6 Treatment Effect Mappings -- 10.7 Covariate-Adjusted Response-Adaptive Randomization -- 10.8 Problems -- 10.9 References -- 10.10 Appendix -- Chapter 11 Inference for Response-Adaptive Randomization -- 11.1 Introduction -- 11.2 Population-Based Inference -- 11.2.1 The likelihood -- 11.2.2 Sufficiency -- 11.2.3 Bias of the maximum likelihood estimators -- 11.2.4 Confidence interval procedures -- 11.3 Power -- 11.3.1 The relationship between power and the variability of the design -- 11.3.2 Asymptotically best procedures -- 11.3.3 Response-adaptive randomization and sequential monitoring -- 11.4 Randomization-Based Inference -- 11.5 Problems -- 11.6 References -- Chapter 12 Response-Adaptive Randomization in Practice -- 12.1 Basic Assumptions -- 12.2 Bias, Masking, and Consent -- 12.3 Logistical Issues -- 12.4 Selection of A Procedure -- 12.5 Benefits of Response-Adaptive Randomization -- 12.6 Some Examples -- 12.6.1 The extracorporeal membrane oxygenation trial -- 12.6.2 The fluoxetine trial -- 12.7 Conclusions -- 12.8 Problems -- 12.9 References -- Author Index -- Subject Index -- Wiley Series in Probability and Statistics -- EULA.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.