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.
"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."
"The book, edited by three leading experts in methodology of clinical trials, includes 18 chapters divided in six sections written by 36 statisticians and other methodologists with longstanding experience in academia, industry, funding agencies, and regulatory authorities. The book covers all aspects of clinical trial design from the early stages of assay validation to later phase I, II, and III trials. It also provides guidance on statistical methods for the analysis of data generated at the different stages. This comprehensive coverage of the“natural history” of drug and biomarker development can help researchers better understand the challenges encountered by their colleagues in previous or subsequent phases of this pipeline. Despite being an edited book, the concepts are well-integrated across the chapters and, most of the time, the transition from one topic to the other is done in a natural way for the reader. . . . I found the book a very informative introduction to a new kind of clinical trials that will become more common in the future years as the community intensifies the pursuit of “individualized treatments.”
~The International Biometric Society
"In reading the book, one gains somewhat of a “double education”, in that through reading about the statistical concepts used in a setting, one’s understanding of the disease biology is enriched, and vice versa."
~Emma YuWang, International Society for Clinical Biostatistics
Clinical Trials for Predictive Medicine: New Paradigms and Challenges
Richard Simon, Shigeyuki Matsui, and Marc Buyse
An Industry Statistician’s Perspective on Personalized Health Care Drug Development
Jane Fridlyand, Ru-Fang Yeh, Howard Mackey, Thomas Bengtsson, Paul Delmar, Greg Spaniolo, and Grazyna Lieberman
Analytical Validation of In Vitro Diagnostic Tests
Robert Becker, Jr.
EARLY CLINICAL TRIALS USING BIOMARKERS
Phase I Dose-Finding Designs and Their Applicability to Targeted Therapies
Takashi Daimon, Akihiro Hirakawa, and Shigeyuki Matsui
An Overview of Phase II Clinical Trial Designs with Biomarkers
Lisa McShane and Sally Hunsberger
Bayesian Adaptive Methods for Clinical Trials of Targeted Agents
Outcome-Adaptive Randomization in Early Clinical Trials
Edward Korn and Boris Freidlin
Challenges of Using Predictive Biomarkers in Clinical Trials
Sumithra Mandrekar and Daniel Sargent
PHASE III RANDOMIZED CLINICAL TRIALS USING BIOMARKERS
Comparison of Randomized Clinical Trial Designs for Targeted Agents
Antje Hoering, Mike LeBlanc, and John Crowley
Phase III All-Comers Clinical Trials with a Predictive Biomarker
Shigeyuki Matsui, Yuki Choai, and Takahiro Nonaka
Evaluation of Clinical Utility and Validation of Gene Signatures in Clinical Trials
Stefan Michiels and Federico Rotolo
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
Hisashi Noma and Shigeyuki Matsui
Statistical and Machine-Learning Methods for Class Prediction in High Dimension
Osamu Komori and Shinto Eguchi
Survival Risk Prediction Using High-Dimensional Molecular Data
Harald Binder, Thomas Gerds, and Martin Schumacher
RANDOMIZED TRIALS WITH BIOMARKER DEVELOPMENT AND VALIDATION
Adaptive Clinical Trial Designs with Biomarker Development and Validation
Boris Freidlin and Richard Simon
Development and Validation of Continuous Genomic Signatures in Randomized Clinical Trials
EVALUATION OF SURROGATE BIOMARKERS
Biomarker-Based Surrogate Endpoints
Marc Buyse, Tomasz Burzykowski, Geert Molenberghs, and Ariel Alonso