Design and Analysis of Clinical Trials for Predictive Medicine provides statistical guidance on conducting clinical trials for predictive medicine. It covers statistical topics relevant to the main clinical research phases for developing molecular diagnostics and therapeutics—from identifying molecular biomarkers using DNA microarrays to confirming their clinical utility in randomized clinical trials.
The foundation of modern clinical trials was laid many years before modern developments in biotechnology and genomics. Drug development in many diseases is now shifting to molecularly targeted treatment. Confronted with such a major break in the evolution toward personalized or predictive medicine, the methodologies for design and analysis of clinical trials is now evolving.
This book is one of the first attempts to contribute to this evolution by laying a foundation for the use of appropriate statistical designs and methods in future clinical trials for predictive medicine. It is a useful resource for clinical biostatisticians, researchers focusing on predictive medicine, clinical investigators, translational scientists, and graduate biostatistics students.
INTRODUCTORY OVERVIEW. Clinical Trials for Predictive Medicine: New Paradigms and Challenges. An Industry Statistician’s Perspective on Personalized Health Care Drug Development. Analytical Validation of In Vitro Diagnostic Tests. EARLY CLINICAL TRIALS USING BIOMARKERS. Phase I Dose-Finding Designs and Their Applicability to Targeted Therapies. An Overview of Phase II Clinical Trial Designs with Biomarkers. Bayesian Adaptive Methods for Clinical Trials of Targeted Agents. Outcome-Adaptive Randomization in Early Clinical Trials. Challenges of Using Predictive Biomarkers in Clinical Trials. PHASE III RANDOMIZED CLINICAL TRIALS USING BIOMARKERS. Comparison of Randomized Clinical Trial Designs for Targeted Agents. Phase III All-Comers Clinical Trials with a Predictive Biomarker. Evaluation of Clinical Utility and Validation of Gene Signatures in Clinical Trials. ANALYSIS OF HIGH-DIMENSIONAL DATA AND GENOMIC SIGNATURE DEVELOPMENTS. Statistical Issues in Clinical Development and Validation of Genomic Signatures. Univariate Analysis for Gene Screening: Beyond Multiple Testing. Statistical and Machine-Learning Methods for Class Prediction in High Dimension. Survival Risk Prediction Using High-Dimensional Molecular Data. RANDOMIZED TRIALS WITH BIOMARKER DEVELOPMENT AND VALIDATION. Adaptive Clinical Trial Designs with Biomarker Development and Validation. Development and Validation of Continuous Genomic Signatures in Randomized Clinical Trials. EVALUATION OF SURROGATE BIOMARKERS. Biomarker-Based Surrogate Endpoints.
"To say this is an extremely timely book would be a gross understatement. The editors have assembled an impressive series of articles that address many of the major methodological issues confronting researchers who are attempting to not only identify valuable medical treatments but to determine which subsets of patients will benefit from these treatments. These issues are particularly important in cancer research, where treatments are often burdensome, toxic, and expensive; one does not want to treat large numbers of patients when only a few will benefit …[this] would be a very valuable resource for anyone in, or moving into, the area of clinical trials for developing targeted therapies. Because the chapters steer away from highly technical discussions, I would recommend this book to clinicians as well as statisticians who are working in this challenging and developing area of research." -Susan Ellenberg, Journal of the ~American Statistical Association
"Design and Analysis of Clinical Trials for Predictive Medicine addresses a necessity for precision medicine: identifying and testing molecular biomarkers for their ability to predict the effect of treatments on specific patient populations."
~Journal of Clinical Research Best Practices, December 2015
"… a very good collection of relevant topics and methods, which have been published and applied in the field of personalized medicine from a clinical development perspective. The editors of this book are experts in the field and have published extensively on these topics. … a useful reference for biostatisticians and researchers involved in the design and analyses of biomarker integrated clinical trials."
~Arunava Chakravartty, Journal of Biopharmaceutical Statistics
"… a useful resource for clinical biostatisticians, researchers focusing on predictive medicine, clinical investigators, translational scientists, and graduate biostatistics students."