A unique overview that melds the concepts of conditionalprobability and stochastic processes into real-lifeapplications
The role of randomization techniques in clinical trials has becomeincreasingly important. This comprehensive guide combines both theapplied aspects of randomization in clinical trials with aprobabilistic treatment of properties of randomization. Taking anunabashedly non-Bayesian and nonparametric approach to inference, the book focuses on the linear rank test under a randomizationmodel, with added discussion on likelihood-based inference as itrelates to sufficiency and ancillarity. Developments in stochasticprocesses and applied probability are also given where appropriate.Intuition is stressed over mathematics, but not without a cleardevelopment of the latter in the context of the former.
Providing a consolidated review of the field, the book includesrelevant and practical discussions of:
* The benefits of randomization in terms of reduction of bias
* Randomization as a basis for inference
* Covariate-adaptive and response-adaptive randomization
* Current philosophies, controversies, and new developments
With ample problem sets, theoretical exercises, and short computersimulations using SAS, Randomization in Clinical Trials: Theory and Practice is equally useful as a standard textbook in biostatisticsgraduate programs as well as a reliable reference forbiostatisticians in practice.
Tabella dei contenuti
Preface.
Randomization and the Clinical Trial.
Issues in the Design of Clinical Trials.
Randomization for Balancing Treatment Assignments.
Balancing on Known Covariates.
The Effects of Unobserved Covariates.
Selection Bias.
Randomization as a Basis for Inference.
Inference for Stratified, Blocked, and Covariate-Adjusted Analyses.
Randomization in Practice.
Response-Adaptive Randomization.
Inference for Response-Adaptive Rondomization.
Response-Adaptive Randomization in Practice.
Some Useful results in Large Sample Theory.
Large Sample Inference for Complete and Restricted Randomization.
Large sample Inference for Response-Adaptive Randomization.
Author Index.
Subject Index.
Circa l’autore
WILLIAM F. ROSENBERGER is an associate professor (with tenure) ofmathematics and statistics at The University of Maryland, Baltimore County. He is also an adjunct associate professor of epidemiologyand preventive medicine at the University of Maryland School of Medicine. He serves as a biostatistical consultant on severalclinical trials data and safety monitoring boards for the NIH, VA, and industry. He received his Ph D in mathematical statistics from The George Washington University.
JOHN M. LACHIN III is presently Professor of Biostatistics and Epidemiology, and of Statistics, at The George Washington University. He holds a Sc D in biostatistics from the University of Pittsburgh. He also serves as Director of the Graduate Program in Biostatistics and as Director of the Coordinating Center for twonationwide studies in diabetes.
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