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Pharmaceutical Statistics Using SAS: A Practical Guide

Contributor(s): Dmitrienko, Alex (Author), Chuang-Stein, Christy (Author), D'Agostino, Ralph (Author)

ISBN: 9781590478868

Publisher: SAS Institute

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Pub Date: February 7, 2007

Dewey: 615.50724

LCCN: 2007275520

Lexile Code: 0000

Features: Index, Table of Contents

Target Age Group: NA to NA

Physical Info: 0.85" H x 10.95" L x 8.43" W ( 2.29 lbs) 460 pages

Series: SAS Press

Descriptions, Reviews, etc.

Description: Offering extensive coverage of cutting-edge biostatistical methodology used in drug development, this reference explores the practical problems facing today's drug developers. It provides relevant tutorial material and SAS examples.

Brief description: Alex Dmitrienko, Ph.D., is Principal Research Scientist, Eli Lilly and Company. He has been actively involved in biostatistical research and has published papers on multiple testing, group sequential inferences, and analysis of categorical data with clinical trial applications. Alex co-authored a recently published SAS Press book, Analysis of Clinical Trials Using SAS: A Practical Guide. His other interests include software implementation of new and existing statistical methods.

Review Quotes: "Pharmaceutical Statistics Using SAS contains applications of cutting-edge statistical techniques using cutting-edge software tools provided by SAS. The theory is presented in down-to-earth ways, with copious examples, for simple understanding. For pharmaceutical statisticians, connections with appropriate guidance documents are made; the connections between the document and the data analysis techniques make 'standard practice' easy to implement. In addition, the included references make it easy to find these guidance documents that are often obscure. Specialized procedures, such as easy calculation of the power of nonparametric and survival analysis tests, are made transparent, and this should be a delight to the statistician working in the pharmaceutical industry, who typically spends long hours on such calculations. However, non-pharmaceutical statisticians and scientists will also appreciate the treatment of problems that are more generally common, such as how to handle dropouts and missing values, assessing reliability and validity of psychometric scales, and decision theory in experimental design. I heartily recommend this book to all." -- Peter H. Westfall, Professor of Statistics "Texas Tech University"

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