Multiple Testing Problems in Pharmaceutical Statistics
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This book explains how to solve the most important problems in multiple testing encountered in drug discovery, pre-clinical, and clinical trial applications. It presents relevant statistical methodology; illustrates the methodology using real-life examples from drug discovery experiments, pre-clinical studies, and clinical trials; and provides software code (SAS, S-Plus, R) for solving the problems. It describes statistical strategies for handling multiplicity issues in the pharmaceutical industry and covers basic/traditional multiple testing procedures as well as novel approaches to…mehr

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Produktbeschreibung
This book explains how to solve the most important problems in multiple testing encountered in drug discovery, pre-clinical, and clinical trial applications. It presents relevant statistical methodology; illustrates the methodology using real-life examples from drug discovery experiments, pre-clinical studies, and clinical trials; and provides software code (SAS, S-Plus, R) for solving the problems. It describes statistical strategies for handling multiplicity issues in the pharmaceutical industry and covers basic/traditional multiple testing procedures as well as novel approaches to performing multiple inferences. The book also discusses regulatory issues.
Bringing together leading statisticians, scientists, and clinicians from the pharmaceutical industry, academia, and regulatory agencies, this volume explores the rapidly growing area of multiple comparison research with an emphasis on pharmaceutical applications. It describes important multiplicity problems encountered in pre-clinical and clinical trial settings. The book includes numerous case studies from actual pre-clinical experiments and clinical trials to help readers quickly learn common multiple testing methods and apply them to real-life problems. It also reviews relevant regulatory guidelines and implements the methods using SAS and R. The data sets and code are available on the book's website.
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Autorenporträt
Alex Dmitrienko is a research advisor in Global Statistical Sciences at Eli Lilly and Company in Indianapolis, Indiana. Ajit C. Tamhane is senior associate dean and professor of industrial engineering and management sciences in the McCormick School of Engineering and Applied Science at Northwestern University in Illinois. Frank Bretz is a biometrical fellow of clinical information sciences at Novartis Pharma AG in Switzerland. He is also an adjunct professor at Hannover Medical School in Germany.