Pharmaceutical Statistics Using SAS: A Practical Guide (SAS Press)

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 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: 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. This book is part of the SAS Press program. Statistics in drug development by Christy Chuang-Stein and Ralph D'Agostino Modern classification methods for drug discovery by Kjell Johnson and William Rayens Model building techniques in drug discovery by Kimberly Crimin and Thomas Vidmar Statistical considerations in analytical method validation by Bruno Boulanger, Viswanath Devanaryan, Walthère Dewé, and Wendell Smith Some statistical considerations in nonclinical safety assessment by Wherly Hoffman, Cindy Lee, Alan Chiang, Kevin Guo, and Daniel Ness Nonparametric methods in pharmaceutical statistics by Paul Juneau Optimal design of experiments in pharmaceutical applications by Valerii Fedorov, Robert Gagnon, Sergei Leonov, and Yuehui Wu Analysis of human pharmacokinetic data by Scott Patterson and Brian Smith Allocation in randomized clinical trials by Olga Kuznetsova and Anastasia Ivanova Sample-size analysis for traditional hypothesis testing: concepts and issues by Ralph G. O'Brien and John Castelloe Design and analysis of dose-ranging clinical studies by Alex Dmitrienko, Kathleen Fritsch, Janson Hsu, and Stephen Ruberg Analysis of incomplete data by Geert Molenberghs, Caroline Beunckens, Herbert Thijs, Ivy Jansen, Geert Verbeke, Michael Kenward, and Kristen Van Steen Reliability and validity: Assessing the psychometric properties of rating scales by Douglas Faries and Ilker Yalcin Decision analysis in drug development by Carl-Fredrik Burman, Andy Grieve, and Stephen Senn. Introduces A Range Of Data Analysis Problems Encountered In Drug Development And Illustrates Them Using Case Studies From Actual Pre-clinical Experiments And Clinical Studies. Includes A Discussion Of Methodological Issues, Practical Advice From Subject Matter Experts, And Review Of Relevant Regulatory Guidelines.