Adaptive Design Theory and Implementation Using SAS and R (Chapman & Hall/CRC Biostatistics Series)
Adaptive design has become an important tool in modern pharmaceutical research and development. Compared to a classic trial design with static features, an adaptive design allows for the modification of the characteristics of ongoing trials based on cumulative information. Adaptive designs increase the probability of success, reduce costs and the time to market, and promote accurate drug delivery to patients. Reflecting the state of the art in adaptive design approaches, Adaptive Design Theory and Implementation Using SAS and R provides a concise, unified presentation of adaptive design theories, uses SAS and R for the design and simulation of adaptive trials, and illustrates how to master different adaptive designs through real-world examples. The book focuses on simple two-stage adaptive designs with sample size re-estimation before moving on to explore more challenging designs and issues that include drop-loser, adaptive dose-funding, biomarker-adaptive, multiple-endpoint adaptive, response-adaptive randomization, and Bayesian adaptive designs. In many of the chapters, the author compares methods and provides practical examples of the designs, including those used in oncology, cardiovascular, and inflammation trials. Equipped with the knowledge of adaptive design presented in this book, you will be able to improve the efficiency of your trial design, thereby reducing the time and cost of drug development.
excellent topic, well covered, with software for implementation
By Michael R. Chernick "statman31147" - July 18, 2007
This book just came out but I know a lot about it and about the author before I even got a copy. In November of last year Mark Chang coauthored a book in this Chapman and Hall series that I reviewed with praise because of the importance of the topic and the way it was demonstrated to work in a variety of real problems in pharmaceutical clinical trials. This book is even better as it goes more deeply into the methodology, the controversies and the results from simulation studies. Also it is much more practical because for every case where an application is given a SAS macro is also included to allow the reader to try the methodology for himself. In March of 2007 I actually designed a two-stage adaptive design with sample size reestimation for bioequivalence trials. I met mark at a conference where he presented much of his recent work and he was instrumental in helping me through his first book and his journal articles. This book had already gone to the publisher but he realized... read more
Comprehensive, concise, unified presentation written by a hands-on statistician with years of adaptive design experiences
By M. Chang - February 16, 2008
There are explosions of adaptive design papers in past several years. This book alone has included about 400 references. It is very confusing to most new researchers in this field. This book use a unified approach to treat the major hypothesis test based adaptive design methods, i.e., view different methods as some forms of stagewise p-values combinations for test statistics. Chapter 1 provides overview of adaptive designs. Chapter 2 provides background for various clinical trials including superior, non-inferiority, equivalence and dose-response trials. The unified approach is presented in chapter 3 for stopping boundary determination, adjusted p-value, early futility and efficacy stopping, expected sample-size and clinical trial duration, conditional power, and futility index. All the formulations for these operating characteristics are presented in multiple-integration forms. In the next several chapters, all the integrations for the operating characteristics are carried out for... read more
A SAS macro library with attached documentation and a few R functions appended
By andreas27 "andreas27" - December 11, 2007
The book, going by the table of contents, provides a fairly comprehensive overview of the field of adaptive designs in drug development. After having read it, I am somewhat disappointed. The topics are in fact all there, and the different approaches are presented. There is no real overview on how the different approaches link together though. I think that other texts like Ting (Dose Finding in Drug Development (Statistics for Biology and Health)) do a much better job at providing the background.
The code seems quite useful, but the typesetting is fairly disastrous. Most functions and macros have many parameters, and they are listed in floating text style instead of a tabular layout, making it very hard to read. The code is typeset in proportional font (where monospace is standard) and does not contain any comments and documentation of particular blocks.
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