Statistical Monitoring of Clinical Trials: A Unified Approach PDF

Rating: (2 reviews) Author: Michael A. Proschan ISBN : 9780387300597 New from $26.00 Format: PDF

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The approach taken in this book is, to studies monitored over time, what the Central Limit Theorem is to studies with only one analysis. Just as the Central Limit Theorem shows that test statistics involving very different types of clinical trial outcomes are asymptotically normal, this book shows that the joint distribution of the test statistics at different analysis times is asymptotically multivariate normal with the correlation structure of Brownian motion ("the B-value") – irrespective of the test statistic. Thus, this book offers statisticians an accessible, incremental approach to understanding Brownian motion as related to clinical trials.

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• Product Details
• Table of Contents
• Reviews

• Series: Statistics for Biology and Health
• Hardcover: 268 pages
• Publisher: Springer; 1 edition (December 6, 2007)
• Language: English
• ISBN-10: 0387300597
• ISBN-13: 978-0387300597
• Product Dimensions: 0.7 x 6.3 x 9.1 inches
• Shipping Weight: 1.1 pounds (View shipping rates and policies)

Statistical Monitoring of Clinical Trials: A Unified Approach PDF

This is one of two excellent books on group sequential methods and adaptive designs. All three authors are ASA Fellows. Wittes and Proschan have worked at the NIH and Proschan formerly worked at the FDA. Gordon Lan has published widely on group sequential methods and has developed software with David deMets that can be downloaded for free from deMets' website at the University of Wisconsin. Lan and deMets developed the theory of alpha spending functions that are commonly used in software such a EaSt (Cytel Corporation) to help determine an appropriate shape to the stopping boundary. Two group sequential methods with markedly different spending functions are the Pocock design anf the O'Brien-Fleming design. I have written a detailed book review for Technometrics, that also compares the book to Jennison and Turnbull's text. Both of these books will be classics. my review appeared in the May 2007 issue of Technometrics.

By Michael R. Chernick

The topics listed in the table of contents of the book are really interesting. On the other hand, the notations used in the book make it so much more difficult to read. For example, the letters chosen for many variables used have no relation at all with their meaning, so that constantly, throughout the book, you need to go back to previous chapters. Also, various statistical results are just used, without any proof or at least hints for proof. Therefore, one needs to read this book while having another graduate stats inference book at hand.

By Tess