Randomization in Clinical Trials: Theory and Practice(Wiley Series in Probability and Statistics)

A unique overview that melds the concepts of conditional probability and stochastic processes into real-life applications The role of randomization techniques in clinical trials has become increasingly important. This comprehensive guide combines both the applied aspects of randomization in clinical trials with a probabilistic treatment of properties of randomization. Taking an unabashedly non-Bayesian and nonparametric approach to inference, the book focuses on the linear rank test under a randomization model, with added discussion on likelihood-based inference as it relates to sufficiency and ancillarity. Developments in stochastic processes and applied probability are also given where appropriate. Intuition is stressed over mathematics, but not without a clear development of the latter in the context of the former. Providing a consolidated review of the field, the book includes relevant 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 computer simulations using SAS, Randomization in Clinical Trials: Theory and Practice is equally useful as a standard textbook in biostatistics graduate programs as well as a reliable reference for biostatisticians in practice.

Table of Contents:
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.

About the Author :
WILLIAM F. ROSENBERGER is an associate professor (with tenure) of mathematics and statistics at The University of Maryland, Baltimore County. He is also an adjunct associate professor of epidemiology and preventive medicine at the University of Maryland School of Medicine. He serves as a biostatistical consultant on several clinical trials data and safety monitoring boards for the NIH, VA, and industry. He received his PhD 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 ScD 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 two nationwide studies in diabetes.

Review :
"…excellent for learning on how to use randomization ideas…" (Journal of Statistical Computation & Simulation, May 2004) "This book is one of the first to devote a substantial part of its content to the theory and practice of the techniques of response-adaptive design in the context of randomized clinical trials." (Apria Healthcare) "...should be very useful in graduate courses...a valuable reference work..." (Short Book Reviews, 2004) "All medical statisticians involved in clinical trials should read this book...." (Controlled Clinical Trials) “..combines the applied aspects of randomization in clinical trials with a probabilistic treatment of properties of randomization...” (Quarterly of Applied Mathematics, Vol. LXI, No. 2, June 2003)