Useful Statistical Approaches for Addressing Multiplicity Issues
Includes practical examples from recent trials
Bringing together leading statisticians, scientists, and clinicians from the pharmaceutical industry, academia, and regulatory agencies, Multiple Testing Problems in Pharmaceutical Statistics explores the rapidly growing area of multiple comparison research with an emphasis on pharmaceutical applications. In each chapter, the expert contributors describe important multiplicity problems encountered in pre-clinical and clinical trial settings.
The book begins with a broad introduction from a regulatory perspective to different types of multiplicity problems that commonly arise in confirmatory controlled clinical trials, before giving an overview of the concepts, principles, and procedures of multiple testing. It then presents statistical methods for analyzing clinical dose response studies that compare several dose levels with a control as well as statistical methods for analyzing multiple endpoints in clinical trials. After covering gatekeeping procedures for testing hierarchically ordered hypotheses, the book discusses statistical approaches for the design and analysis of adaptive designs and related confirmatory hypothesis testing problems. The final chapter focuses on the design of pharmacogenomic studies based on established statistical principles. It also describes the analysis of data collected in these studies, taking into account the numerous multiplicity issues that occur.
This volume explains how to solve critical issues in multiple testing encountered in pre-clinical and clinical trial applications. It presents the necessary statistical methodology, along with examples and software code to show how to use the methods in practice.
Table of Contents
Multiplicity Problems in Clinical Trials: A Regulatory Perspective. Multiple Testing Methodology. Multiple Testing in Dose Response Problems. Analysis of Multiple Endpoints in Clinical Trials. Gatekeeping Procedures in Clinical Trials. Adaptive Designs and Confirmatory Hypothesis Testing. Design and Analysis of Microarray Experiments for Pharmacogenomics. Bibliography.
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.