Main page > Education > Books

Edited by Alex Dmitrienko Principal Research Scientist Eli Lilly and Company Christy Chuang-Stein Executive Director Pfizer Ralph D'Agostino Professor of Mathematics/Statistics and Public Health Boston University Copyright © 2007 SAS Institute Inc. Used with permission.

If you would like to share comments or feedback, we encourage you to post relevant information in this discussion thread in the BioPharmNet forum.

Book's description

Table of contents

Praise from the experts

Other books by the authors

Book's web page on the SAS web site

The book consists of 14 chapters motivated by data analysis problems arising at various stages of drug development:
- drug discovery experiments to identify promising chemical compounds,
- animal studies to assess the toxicological profile of these compounds,
- clinical 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.
The book offers a broad coverage of biostatistical methodology used in drug development and practical problems facing today's drug developers. It provides 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. It introduces a wide range of data analysis problems encountered in drug development and illustrates them using a large number of case studies from actual pre-clinical experiments and clinical studies, and provides SAS code for solving the problems. The book also features 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. It will also serve as a useful reference for regulatory scientists as well as academic researchers and graduate students.

Chapter 1. Statistics in Drug Development
Christy Chuang-Stein, Ralph D'Agostino

Chapter 2. Modern Classification Methods for Drug Discovery
Kjell Johnson, William Rayens

Chapter 3. Model Building Techniques in Drug Discovery
Kimberly Crimin, Thomas Vidmar

Chapter 4. Statistical Considerations in Analytical Method Validation
Bruno Boulanger, Viswanath Devanaryan, Walthere Dewe, Wendell Smith

Chapter 5. Some Statistical Considerations in Nonclinical Safety Assessment
Wherly Hoffman, Cindy Lee, Alan Chiang, Kevin Guo, Daniel Ness

Chapter 6. Nonparametric Methods in Pharmaceutical Statistics
Paul Juneau

Chapter 7. Optimal Design of Experiments in Pharmaceutical Applications
Valerii Fedorov, Robert Gagnon, Sergei Leonov, Yuehui Wu

Chapter 8. Analysis of Human Pharmacokinetic Data
Scott Patterson, Brian Smith

Chapter 9. Allocation in Randomized Clinical Trials
Olga Kuznetsova, Anastasia Ivanova

Chapter 10. Sample-Size Analysis for Traditional Hypothesis Testing: Concepts and Issues
Ralph O'Brien, John Castelloe

Chapter 11. Design and Analysis of Dose-Ranging Clinical Studies
Alex Dmitrienko, Kathleen Fritsch, Jason Hsu, Stephen Ruberg

Chapter 12. Analysis of Incomplete Data
Geert Molenberghs, Caroline Beunckens, Herbert Thijs, Ivy Jansen, Geert Verbeke, Michael Kenward, Kristel Van Steen

Chapter 13. Reliability and Validity: Assessing the Psychometric Properties of Rating Scales
Douglas Faries, Ilker Yalcin

Chapter 14. Decision Analysis in Drug Development
Carl-Fredrik Burman, Andy Grieve, Stephen Senn

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.

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.

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.

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.

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 website, this book will be a very useful resource for statisticians in the pharmaceutical industry.

The first thing that catches one 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.

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 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 experts 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.