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Title: The Use of Restricted Significance Tests in Clinical Trials (Springer Series in Statistics. Statistics in the Health Sciences) by David S. Salsburg ISBN: 0-387-97798-8 Publisher: Springer-Verlag Telos Pub. Date: 01 October, 1992 Format: Hardcover Volumes: 1 List Price(USD): $99.00

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Average Customer Rating: 5 (1 review)

Rating: 5
Summary: a case for restricted tests in clinical trials
Comment: David Salsburg is a statistician with a good knowledge of the foundations of statistics as well as being a practical statistician with a great deal of clinical trials experience. In Chapter 1 he discusses randomized controlled clinical trials. Chapters 2-5 deal with foundational issues in probability and statistics. It leads to the conclusion that hypothesis testing is appropriate for clinical trials and statistical power against alternatives is an important concept for choosing appropriate test statistics but (1)for most practical problems uniformly most powerful tests do not exist and (2)powerful tests can only be found if one is able to meaningfully restrict the class of alternative hypotheses being considered.

In Chapter 6, Salsburg makes the case for restricted tests by providing a real clinical example. Special methods are then covered in the remaining chapters. Chapter 8 deals with resampling approaches including permutation methods and the bootstrap. In Chapter 10, Neyman's often neglected theory of restricted chi-squared tests, is presented.

This is a well written and unusual book that covers methodology not seen in very many biostatistics books. However, these techniques are very relevant to the clinical trials commonly conducted at pharmaceutical companies. It is an important reference source for biostatisticians.

Those interested in statistical theory and its foundational issues will find clear and concise coverage in the first 5 chapters. However if you want more, take a look at Salsburg's new book "The Lady Tasting Tea" which just came out in 2001. In that book he raises all the same issues and more in the context of discussing the great statisticians of the 20th Century.