Pharmaceutical Statistics Using SAS: A Practical Guide 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 Alex Dmitrienko, Christy Chuang-Stein, and Ralph D'Agostino, 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, 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. 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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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.
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