Multiple Testing Problems in Pharmaceutical Statistics

Useful Statistical Approaches for Addressing Multiplicity IssuesIncludes 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 Statisticsexplores 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.

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.

Multiplicity Problems in Clinical Trials: A Regulatory Perspective, Mohammad Huque and Joachim Röhmel
Introduction Common multiplicity problems in clinical trials Reducing multiplicity in clinical trials Multiplicity concerns in special situations Multiplicity in the analysis of safety endpoints Concluding remarks
Multiple Testing Methodology, Alex Dmitrienko, Frank Bretz, Peter H. Westfall, James Troendle, Brian L. Wiens, Ajit C. Tamhane, and Jason C. Hsu
Introduction Error rate definitions Multiple testing principlesAdjusted significance levels, p-values, and confidence intervals Common multiple testing procedures Multiple testing procedures based on univariate p-values Parametric multiple testing procedures Resampling-based multiple testing procedures Software implementation
Multiple Testing in Dose Response Problems, Frank Bretz, Ajit C. Tamhane, and José Pinheiro
Introduction Dose response trend testsTarget dose estimation using multiple hypothesis testing Power and sample size calculation for target dose estimation Hybrid approaches combining multiple testing and modelingAnalysis of Multiple Endpoints in Clinical Trials, Ajit C. Tamhane and Alex Dmitrienko
Introduction Inferential goals At-least-one procedures Global testing procedures All-or-none procedures Superiority-noninferiority procedures Software implementationGatekeeping Procedures in Clinical Trials, Alex Dmitrienko and Ajit C. Tamhane
Introduction Motivating examples Serial gatekeeping proceduresParallel gatekeeping procedures Tree gatekeeping procedures Software implementation

Adaptive Designs and Confirmatory Hypothesis Testing, Willi Maurer, Michael Branson, and Martin Posch
Introduction Basic principles and methods of error rate control Principles of adaptive testing procedures Adaptive multiple testing procedures Case studies Discussion
Design and Analysis of Microarray Experiments for Pharmacogenomics, Jason C. Hsu, Youlan Rao, Yoonkyung Lee, Jane Chang, Kristin Bergsteinsdottir, Magnus Karl Magnússon, Tao Wang, and Eirikur Steingrímsson
Potential uses of biomarkers Clinical uses of genetic profiling Two stages of pharmacogenomic development Multiplicity in pharmacogenomics Designing pharmacogenomic studiesAnalyzing microarray data by modeling A proof of concept experiment Software implementationBibliography