The Theory of Response-Adaptive Randomization in Clinical Trials
Copyright © 2006 John Wiley & Sons, Inc. All rights reserved.
Author(s): Feifang Hu, William F. Rosenberger
Published Online: 10 APR 2006
Print ISBN: 9780471653967
Online ISBN: 9780470055885
Book Series: Wiley Series in Probability and Statistics
About this Book
Presents a firm mathematical basis for the use of response-adaptive randomization procedures in practice
The Theory of Response-Adaptive Randomization in Clinical Trials is the result of the authors' ten-year collaboration as well as their collaborations with other researchers in investigating the important questions regarding response-adaptive randomization in a rigorous mathematical framework. Response-adaptive allocation has a long history in biostatistics literature; however, largely due to the disastrous ECMO trial in the early 1980s, there is a general reluctance to use these procedures.
This timely book represents a mathematically rigorous subdiscipline of experimental design involving randomization and answers fundamental questions, including:
- How does response-adaptive randomization affect power?
- Can standard inferential tests be applied following response-adaptive randomization?
- What is the effect of delayed response?
- Which procedure is most appropriate and how can "most appropriate" be quantified?
- How can heterogeneity of the patient population be incorporated?
- Can response-adaptive randomization be performed with more than two treatments or with continuous responses?
The answers to these questions communicate a thorough understanding of the asymptotic properties of each procedure discussed, including asymptotic normality, consistency, and asymptotic variance of the induced allocation. Topical coverage includes:
- The relationship between power and response-adaptive randomization
- The general result for determining asymptotically best procedures
- Procedures based on urn models
- Procedures based on sequential estimation
- Implications for the practice of clinical trials
Useful for graduate students in mathematics, statistics, and biostatistics as well as researchers and industrial and academic biostatisticians, this book offers a rigorous treatment of the subject in order to find the optimal procedure to use in practice.