Pharmaceutical Statistics Using SAS: A Practical Guide

Editorial Reviews

Product Details
/ u, p" W, Q! X: v2 T2 pPaperback: 464 pages
/ F8 J: G, s6 J' \# F' ~Publisher: SAS Publishing; 1 edition (February 26, 2007)
Language: English
ISBN-10: 159047886X
3 T  d/ C$ f* m6 X+ l5 [5 JISBN-13: 978-1590478868
Product Dimensions: 10.8 x 8.4 x 0.5 inches
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/ @( g- b' y/ ~- H5 b7 t* JReview
. d+ ?* b; q" p6 o- u"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

. q: v/ e" o; i$ w4 S8 s"The book should be a very useful guide for practicing statisticians. What impressed me most was its breadth; it covers all stages of drug development, from preclinical testing to early clinical studies and late-stage clinical studies. The editors have pulled together an excellent set of authors, including experts from the pharmaceutical industry and prominent academics." --Steve Snapinn, Executive Director, Clinical Development Biostatistics Amgen, Inc.

9 h1 A& x9 H% ~$ C, F8 e"This book is an ideal overview of some of the many important issues arising in the pharmaceutical industry, and can be read as such. Students anticipating a career in pharmaceutical statistics will benefit particularly: these are topics that form the backbone of statistics in the industry but that are not generally taught as part of an M.S. or Ph.D. program. Implementation using SAS is admirably detailed, but even non-users of SAS will find the book useful." --Raymond J. Carroll, Distinguished Professor of Statistics, Nutrition and Toxicology, Texas A&M University

6 u$ ?! [" D" w3 \Product Description
6 @& ]% w4 y8 R3 b" RThis essential new book offers extensive coverage of cutting-edge biostatistical methodology used in drug development and the practical problems facing today's drug developers. Written by well-known experts in the pharmaceutical industry, it provides relevant tutorial material and SAS examples to help readers new to a certain area of drug development quickly understand and learn popular data analysis methods and apply them to real-life problems. Step-by-step, the book introduces a wide range of data analysis problems encountered in drug development and illustrates them using a wealth of case studies from actual pre-clinical experiments and clinical studies. The book also provides SAS code for solving the problems. Among the topics addressed are these: drug discovery experiments to identify promising chemical compounds
animal studies to assess the toxicological profile of these compounds
  b. n8 W8 r* z4 x9 iclinical pharmacology studies to examine the properties of new drugs in healthy human subjects
Phase II and Phase III clinical trials to establish therapeutic benefits of experimental drugs
Additional features include a discussion of methodological issues, practical advice from subject-matter experts, and review of relevant regulatory guidelines. Most chapters are self-contained and include a fair amount of high-level introductory material to make them accessible to a broad audience of pharmaceutical scientists. This book will also serve as a useful reference for regulatory scientists as well as academic researchers and graduate students.

                               
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ISBN: 978-1-59047-886-8 Pharmaceutical Statistics Using SAS: A Practical Guide.
- A! n& {7 ]) O0 JI just received this book to review for the journal Technometrics. If you want to see a thorough professional review of all the chapters wait for the journal book reviews be me and others that will be forthcoming over the next couple of years. But if you just want to know now whether or not this book is for you read on.
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7 P! ^2 q2 b3 L% e" v$ X  N* b- XThis is a collection of chapters on application of modern statistical methods in the various phases of drug discovery and clinical research with an emphasis on clinical research but also with plenty of good material to satisfy the interest of those doing pre-clinical and non-clinical research. It contains state-of-the-art methodology and the chapters are written by the leading biostatisticians both in industry and academia. It includes an introductory chapter by Chuang-Stein and D'Agostino. The remaining 13 chapters contain specific applications with illustraions and solutions in SAS. This is great for biostatisticians in this industry who usually have SAS as their primary statistics package. But as pointed out by one of the expert reviewers. If you find this book in a bookstore read the first two pages which give you some of the reasons the experts think this is a greta buy. The reviews listed there are by Peter Westfall, Frank Shen, Byron Jones, Jose Pinheiro and Barry Davis.
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* u2 `8 i9 `  y; M9 w: Y  yThe editors of the book are also leading experts and they have loaded the book with material on the various phases of drug development covering all the key topics. Many of the authors are also well-known for the contributions to biostatistics either in industry or academia or both.

I am so enthusiastic about this book that I have already recommended it to many of my friends in the pharmaceutical industry. Although the chapters include state-of-the-art research along with the commonly used procedures, the chapters are well-written and treated in an introductory way. So the book may be accessible to clinical researchers as well as statisticians as a great reference source. In addition to detailed SAS code to implement the methods the chapters also include extensive bibliographies for those wishing to learn the nitty gritty and better understand the theory behind the techniques.

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