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Randomization in Clinical Trials Theory and Practice

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ISBN-10: 0471236268

ISBN-13: 9780471236269

Edition: 2002

Authors: William F. Rosenberger, John M. Lachin

List price: $165.00
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Description:

Randomization is a key element in the development of clinical drugs. This book provides a consolidated review of the field with relevant and practical discussions of applications to government, industry, and academia.
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Book details

List price: $165.00
Copyright year: 2002
Publisher: John Wiley & Sons, Incorporated
Publication date: 7/11/2002
Binding: Hardcover
Pages: 288
Size: 6.50" wide x 9.50" long x 0.75" tall
Weight: 1.496
Language: English

FEIFANG HU, PhD, is Associate Professor in the Department of Statistics at the University of Virginia. He is the recipient of numerous grants, honors, and awards. His research interests include biostatistics, applied probability and statistical inference, and resampling methods. He has authored over forty refereed articles.WILLIAM F. ROSENBERGER, PhD, is Professor and Chair of the Department of Applied and Engineering Statistics at George Mason University. He received the Association of American Publishers' Professional/Scholarly Publishing Award in 2002 for his coauthored work, Randomization in Clinical Trials: Theory And Practice (Wiley). A Fellow of the American Statistical Association, he has authored over fifty refereed articles and edited two monographs.

Preface
Randomization and the Clinical Trial
Introduction
Causation and association
Randomized clinical trials
Ethics of randomization
Problems
References
Issues in the Design of Clinical Trials
Introduction
Study outcomes
Sources of bias
Standardization and masking
Statistical analysis philosophy
Losses to follow-up and noncompliance
Covariates
Experimental design
Recruitment and follow-up
Determining the number of randomized subjects
Development of the main formula
Example
Survival trials
Adjustment for noncompliance
Additional considerations
Problems
References
Randomization for Balancing Treatment Assignments
Introduction
The balancing properties of complete randomization
Random allocation rule
Truncated binomial design
Permuted block designs
Efron's biased coin design
Wei's urn design
Generalized biased coin designs
Comparison of balancing properties
K > 2 treatments
Restricted randomization for unbalanced allocation
Problems
References
Balancing on Known Covariates
Introduction
Stratified randomization
Treatment imbalances in stratified trials
Covariate-adaptive randomization
Zelen's rule
The Pocock-Simon procedure
Wei's marginal urn design
Optimal design based on a linear model
Conclusions
Problems
References
The Effects of Unobserved Covariates
Introduction
A bound on the probability of a covariate imbalance
Accidental bias
Maximum eigenvalue of [Sigma subscript T]
Accidental bias for the biased coin designs
Simulation results
Conclusions
Problems
References
Selection Bias
Introduction
The Blackwell-Hodges model
Selection bias for the random allocation rule
Selection bias for the truncated binomial design
Selection bias in a permuted block design
Permuted blocks using the random allocation rule
Variable block design
Permuted blocks with truncated binomial randomization
Conclusions
Selection bias for Efron's biased coin design
Wei's urn design
Generalized biased coin designs
Controlling selection bias in practice
Problems
References
Randomization as a Basis for Inference
Introduction
The population model
The randomization model
Permutation tests
Linear rank tests
Variance of the linear rank test
Optimal rank scores
Construction of exact permutation tests
Large sample permutation tests
Group sequential monitoring
Problems
References
Appendix A: DCCT Data
Appendix B: SAS Code for Conditional U D(0, 1) Linear Rank Test
Inference for Stratified, Blocked, and Covariate-Adjusted Analyses
Introduction
Stratified analysis
The Mantel-Haenszel procedure
Linear rank test
Small strata
Stratified versus unstratified tests with stratified randomization
Efficiency of stratified randomization in a stratified analysis
Post-hoc stratified and subgroup analyses
Complete randomization
Random allocation rule
Permuted block randomization with a random allocation rule
Wei's urn design
Pre- and post-stratified analyses
Analyses with missing data
Covariate-adjusted analyses
Example 1: The Neonatal Inhaled Nitric Oxide Study
A Blocked Randomization and Analysis
A Post-Stratified Blocked Analysis
Covariate-Adjusted Blocked Analysis
Example 2: The Diabetes Control and Complications Trial
A Stratified Urn Randomization and Analysis
Urn Analysis with Missing Data
Covariate-Adjusted Urn Analysis
Conclusions
Problems
References
Randomization in Practice
Introduction
Stratification
Characteristics of randomization procedures
Consideration of selection bias
Implications for analysis
Choice of randomization procedure
Complete randomization
Forced-balance designs
Permuted block design
Biased coin-type designs
Generation and checking of sequences
Implementation
Packaging and labeling
The actual randomization
Special situations
Some examples
The Optic Neuritis Treatment Trial
Vesnarinone in congestive heart failure
The Diabetes Control and Complications Trial
Captopril in diabetic nephropathy
The Diabetes Prevention Program
Adjuvant chemotherapy for locally invasive bladder cancer
Problems
References
Response-Adaptive Randomization
Introduction
Historical notes
Roots in bandit problems
Roots in sequential stopping problems
Roots in randomization
Optimal allocation
Response-adaptive randomization to target R*
Sequential maximum likelihood procedure
Doubly-adaptive biased coin design
Urn models
The generalized Friedman's urn model
The randomized play-the-winner rule
Ternary urn models
Treatment effect mappings
Problems
References
Inference for Response-Adaptive Randomization
Introduction
Population-based inference
The likelihood
Sufficiency
Bias of the maximum likelihood estimators
Confidence interval procedures
Power
Randomization-based inference
Problems
References
Response-Adaptive Randomization in Practice
Basic assumptions
Bias, masking, and consent
Logistical issues
Selection of a procedure
Benefits of response-adaptive randomization
Some examples
The Extracorporeal Membrane Oxygenation trial
The fluoxetine trial
Conclusions
Problems
References
Some Useful Results in Large Sample Theory
Some useful central limit theorems
Martingales and sums of dependent random variables
Martingales and triangular arrays
Asymptotic normality of maximum likelihood estimators
The likelihood
Basic conditions for consistency and asymptotic normality
Alternative conditions
Conclusions
Problems
References
Large Sample Inference for Complete and Restricted Randomization
Introduction
Complete randomization
The unconditional test
The conditional test
Simulation results
Random allocation rule
Truncated binomial design
Efron's biased coin design
Wei's urn design
Wei, Smythe, and Smith's general allocation rules
The unconditional test for K > 2 treatments
The conditional test for two treatments
Conclusions
Problems
References
Large Sample Inference for Response-Adaptive Randomization
Introduction
Maximum likelihood estimation
Asymptotic normality of the maximum likelihood estimator: Urn models
Delayed response
Likelihood ratio test for K treatments
Asymptotic properties of sequential maximum likelihood procedures
Large sample linear rank tests
Problems
References
Author Index
Subject Index