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| Adaptive Design Methods in Clinical Trials (Biostatistics) | 
 enlarge | Authors: Shein-chung Chow, Mark Chang
Publisher: Chapman & Hall/CRC
Discount Category: Book
Selling Price: $89.95
Buy New: $64.76
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Customer Ratings:  2 comments
Media: Hardcover
Edition: 1
Number Of Items: 1
Pages: 296
Shipping Weight (pounds): 1.1
Dimensions (inch): 9.1 x 6.2 x 0.8
ISBN: 1584887761
Dewey Decimal Number: 610.74
EAN: 9781584887768
Publication Date: November 16, 2006
Availability: Usually ships in 24 hours
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| Customer Comments:
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 This book is a "beta" version January 13, 2008
5 out of 5 found this comment useful.
This book's best features are its bibliography (about 240 entries) and its broad survey and taxonomy of adaptive methods. Its publication represents an important step in popularizing adaptive trials and, thus, streamlining drug/device/biologic development pipelines.
The book is, however, filled with inaccuracies on several levels: incorrect grammar and equation references, undefined symbols, a reference to a non-existent appendix, unclear language (e.g., what is the "statistical strength for rejecting Ho" on Page 150?), mathematical typos [e.g., P(x|y,theta) rather than P(y|theta) in the integrand for the posterior predictive probability distribution P(y|x)], and misapplications of statistical philosophy (e.g., using Neyman-Pearson hypothesis testing for statistical inference, identifying the p-value as a post-hoc type I error rate). In the sample I took of about 1/3 of the pages, about 120 errors occur. The book should be considered only a pre-publication "beta" version. Any second edition should receive much more attention to detail.
A statistician or clinical scientist planning a potentially adaptive trial could use this book to learn about some of the aspects of a trial that can be made adaptive. The book could also help him/her to assess the assumptions and mathematical complexity of methods under consideration. However, when it comes to actually performing an analysis, one would want to use the bibliography to obtain the relevant articles and books, perhaps together with Chang's "Adaptive Design Theory and Implementation Using SAS and R" (Chapman & Hall/CRC Biostatistics).
Overall, this book disappointed me. The authors should have had several more collaborators and copyeditors check their work.
 Great contribution to pharmaceutical industry April 8, 2007
40 out of 42 found this comment useful.
I meet the second author, Mark Chang, at a conference on adaptive designs. I work as a professional statistician in the pharmaceutical industry. For the past several years, at least ten, these ideas have been the topic of research and it is being investigated as a possible way to speed up drug development and its development is being encouraged by the FDA. There has not been a formal statistical text covering the existing theory and its application to clinical trials. Consequently, when we knew this was coming out we preordered it and have been studying it since it came out last November.
The book has lived up to expectations. Adaptive designs are very similar to group sequential designs in that they have planned times to make preliminary assessment of the trial data and then decide whether or not to continue the trial or modify the design. Adaptive designs can be more flexible than their group sequential counterparts. They even can allow changes to the protocol as long as the criteria for making such changes are mapped out in advance of the trial.
These methods have been controversial in the past and simulation studies are often required to determine their properties. But there has been enough development now that some designs are being applied in real trials. In fact we are considering a two stage adaptive design similar to the ones described in this text (except applied to bioequivalence).
Later this year Mark Chang is coming out with an applied text that include SAS macros to aid in the implementation of the methods. A preview of the manuscript was displayed at an adaptive trials conference that I attended recently. I can enthusiastically recommend that one even more than this one! However, any biostatistician working on clinical trials should have this book on his or her bookshelf.
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