Pharmaceutical Statistics Using SAS®: A Practical Guide 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
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
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.
Executive Director, Clinical Development Biostatistics Amgen, Inc.
The book is well written by people well known in the pharmaceutical industry. The selected topics are comprehensive and relevant. Explanations of the statistical theory are concise, and the solutions are up-to-date. It would be particularly useful for isolated statisticians who work for companies without senior colleagues.
Global Biometric Sciences
Bristol-Myers Squibb Co.
This book covers an impressive range of topics in clinical and non-clinical statistics. Adding the fact that all the datasets and SAS code discussed in the book are available on the SAS Web site, this book will be a very useful resource for statisticians in the pharmaceutical industry.
Pfizer Global Research and Development
The first thing that catches one's attention about this very interesting book is its breadth of coverage of statistical methods applied to pharmaceutical drug development. Starting with drug discovery, moving through pre-clinical and non-clinical applications, and concluding with many relevant topics in clinical development, the book provides a comprehensive reference to practitioners involved in, or just interested to learn about, any stage of drug development. There is a good balance between well-established and novel material, making the book attractive to both newcomers to the field and experienced pharmaceutical statisticians. The inclusion of examples from real studies, with SAS code implementing the corresponding methods, in every chapter but the introduction, is particularly useful to those interested in applying the methods in practice, and who certainly will be the majority of the readers. Overall, an excellent addition to the SAS Press collection.
Director of Biostatistics
This is a very well-written, state-of-the-art book that covers a wide range of statistical issues through all phases of drug development. It represents a well-organized and thorough exploration of many of the important aspects of statistics as used in the pharmaceutical industry. The book is packed with useful examples and worked exercises using SAS. The underlying statistical methodology that justifies the methods used is clearly presented.
The authors are clearly expert and have done an excellent job of linking the various statistical applications to research problems in the pharmaceutical industry. Many areas are covered including model building, nonparametric methods, pharmacokinetic analysis, sample size estimation, dose-ranging studies, and decision analysis. This book should serve as an excellent resource for statisticians and scientists engaged in pharmaceutical research or anyone who wishes to learn about the role of the statistician in the pharmaceutical industry.
Barry R. Davis
Professor of Biomathematics
University of Texas