Randomization in Clinical Trials

Theory and Practice
 
 
John Wiley & Sons Inc (Verlag)
  • 2. Auflage
  • |
  • erschienen am 19. Oktober 2015
  • |
  • 288 Seiten
 
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-1-118-74215-0 (ISBN)
 
Praise for the First Edition
"All medical statisticians involved in clinical trials should read this book..."
- Controlled Clinical Trials
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.
weitere Ausgaben werden ermittelt
  • 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 > 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 ST
  • 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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