Multiple testing problems in pharmaceutical statistics, edited by A. Dmitrienko, A.C. Tamhame, and F. Bretz, Boca Raton, Chapman and Hall/CRC, 2010, xvi+304 pp., £57.99 or US$89.95 (hardback), ISBN 978-1-58488-984-7
This book gives an overview of the present multiple hypothesis testing that goes on in the pharmaceutical field. There are many good reviews of multiple hypothesis testing in general, such as Citation2, but little that is solely devoted to the pharmaceutical field.
The book presents the subject matter in a way that is very thorough and is written by some of the top researchers in the field. First it goes into the multiplicity problems that one commonly experiences in controlled trials. In particular, it highlights the difficulties in trials such as those with multiple subgroups. It then addresses the multiple hypothesis problem through the use of the conventional p-value approach as well as the parametric and resampling approaches. Analysis of multiple endpoints is illustrated through the use of tests such as the union-intersection procedure which is used in determining if the treatment is superior on all end points. Gatekeeping procedures and adaptive designs which are commonplace in the pharmaceutical field are also covered.
The book ends with the use of drug efficacy via the use of microarray experiments. While SAS code has been provided, R code has not. I do not view this as a deficiency since the target audience for this book is not researchers but rather practising statisticians in the pharmaceutical field.
A little more work could have gone into describing multiple hypothesis testing in the context of microarrays. The importance and relevance of the false discovery rate Citation1 in studies today should have been highlighted a little more. Overall the book is a good one and libraries should be encouraged to purchase a copy. It will be useful to those researchers in both the biomedical and statistics fields. Practising statisticians in industry stand to benefit most from the book because of its completeness.
References
- Benjamini , Y. and Hochberg , Y. 1995 . Controlling the false discovery rate: A practical and powerful approach to multiple testing . J. Roy. Statist. Soc. B , 57 : 289 – 300 .
- Shaffer , P. J. 1995 . Multiple hypothesis testing . Ann. Rev. Psychol. , 46 : 561 – 584 .