This 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, clinical pharmacology studies to examine the properties of new drugs in healthy human subjects, and 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.
"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"