Randomization in clinical trials theory and practice 2nd Edition by John Lachin,William Rosenberger 9781118742242 1118742249

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ISBN 10:1118742249 
ISBN 13:9781118742242
Author:John Lachin,William Rosenberger

Featuring a unique combination of the applied aspects of randomization in clinical trials with a nonparametric approach to inference, Randomization in Clinical Trials: Theory and Practice, Second Edition is the go-to guide for biostatisticians and pharmaceutical industry statisticians.

Randomization in Clinical Trials: Theory and Practice, Second Edition features:

• Discussions on current philosophies, controversies, and new developments in the increasingly important role of randomization techniques in clinical trials
• A new chapter on covariate-adaptive randomization, including minimization techniques and inference
• New developments in restricted randomization and an increased focus on computation of randomization tests as opposed to the asymptotic theory of randomization tests
• Plenty of problem sets, theoretical exercises, and short computer simulations using SAS® to facilitate classroom teaching, simplify the mathematics, and ease readers’ understanding

Randomization in Clinical Trials: Theory and Practice, Second Edition is an excellent reference for researchers as well as applied statisticians and biostatisticians. The Second Edition is also an ideal textbook for upper-undergraduate and graduate-level courses in biostatistics and applied statistics.

William F. Rosenberger, PhD, is University Professor and Chairman of the Department of Statistics at George Mason University. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and author of over 80 refereed journal articles, as well as The Theory of Response-Adaptive Randomization in Clinical Trials, also published by Wiley.

John M. Lachin, ScD, is Research Professor in the Department of Epidemiology and Biostatistics as well as in the Department of Statistics at The George Washington University. A Fellow of the American Statistical Association and the Society for Clinical Trials, Dr. Lachin is actively involved in coordinating center activities for clinical trials of diabetes. He is the author of Biostatistical Methods: The Assessment of Relative Risks, Second Edition, also published by Wiley.

 

Randomization in clinical trials theory and practice 2nd Table of contents:

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

2 Issues in the Design f 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 

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 

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 

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 

6 Randomization as a Basis for Inference 

6.1 Introduction 

6.2 The Population Model

6.4 Randomization Tests 

7 Stratification 

7.1 Introduction 

7.2 Stratified Randomization 

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  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 

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 

10 Response-Adaptive Randomization 

10.1 Introduction 

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 

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 

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 

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