Adaptive Design Methods in Clinical Trials, Second Edition
Chapman and Hall/CRC – 2012 – 374 pages
With new statistical and scientific issues arising in adaptive clinical trial design, including the U.S. FDA’s recent draft guidance, a new edition of one of the first books on the topic is needed. Adaptive Design Methods in Clinical Trials, Second Edition reflects recent developments and regulatory positions on the use of adaptive designs in clinical trials. It unifies the vast and continuously growing literature and research activities on regulatory requirements, scientific and practical issues, and statistical methodology.
New to the Second Edition
Along with revisions throughout the text, this edition significantly updates the chapters on protocol amendment and clinical trial simulation to incorporate the latest changes. It also includes five entirely new chapters on two-stage adaptive design, biomarker adaptive trials, target clinical trials, sample size and power estimation, and regulatory perspectives.
Following in the tradition of its acclaimed predecessor, this second edition continues to offer an up-to-date resource for clinical scientists and researchers in academia, regulatory agencies, and the pharmaceutical industry. Written in an intuitive style at a basic mathematical and statistical level, the book maintains its practical approach with an emphasis on concepts via numerous examples and illustrations.
"In this second edition, the authors update two chapters on protocol amendment and clinical trial simulation to reflect new developments after the first edition and add five new chapters. … It also provides many useful algorithms for various designs and SAS code throughout the book. … this is an excellent book with wonderful resources on adaptive design methods in clinical trials. It will be very useful to graduate students in the areas of clinical development and biostatistics looking for an advanced textbook on this topic. The book is very well written and a joy to read. I think it would be a critical addition to the bookshelf for statisticians involved in adaptive design and analysis in clinical trials."
—Hongfei Guo, Journal of the American Statistical Association, December 2013
"This second edition remains a useful reference source for anyone interested in advancing innovative trial designs and wishing to incorporate adaptations, modifications, and changes to the drug development process. Five new chapters have been added and are all worth reading; bringing the technical material covered up-to-date. For anyone working in, and studying, clinical research the book is worth purchasing and will make a valuable addition to any library. … this revision continues to provide a balanced summary of statistical methods, together with the authors’ perspective on current regulatory practice."
—International Statistical Review, 80, 2012
Praise for the First Edition
The authors are to be commended for the endeavour to provide a monograph about this rapidly evolving area in- to our knowledge- the first book specifically devoted to adaptive design methods . . . we think it provides a valuable contribution to the area of adaptive design.
—Frank Miller and Stig Johan Wiklund, AstraZeneca, Statistical Medicine, 2008, Vol. 27
In summary, this is an extremely useful book for clinical trials statisticians wishing to stay abreast with the innovative approaches that are being developed amid some controversies regarding their benefits.
—Yuko Palesch, Medical University of South Carolina, Journal of the American Statistical Association, March 2008, Vol. 103, No. 481
…This book is one of the first of its kind to be released solely dealing with adaptive designs in clinical trials. … a useful reference for those who have a mathematical background and wish to understand some of the adaptive design methodologies. …With the ever-increasing need for adaptive trials, we could see this book having a large influence …
—Pharmaceutical Statistics, 2008
…a useful reference source for anyone interested in advancing innovative trial designs and wishing to incorporate adaptations, modifications, and changes to the drug, device, and biological developmental processes.
—C.M. O’Brien, International Statistical Review, Vol. 75, No. 2, 2007
…uses a broad definition of adaptive design methods… .This breadth of coverage is to be commended and makes the book a useful reference and overview for anyone who is starting to explore this area … the book is worth reading.
—Angela Wade, University College London, UK
The book covers a vast area. To my knowledge this book is the first attempt to provide a concise description of various methodologies in a highly dynamic area. The authors deserve to be praised for bringing this huge range of approaches, including very recent trends, together in one book … .
—Tim Friede, University of Warwick, Biometrics, March 2008
What Is Adaptive Design
Target Patient Population
Aims and Scope of the Book
Moving Target Patient Population
Analysis with Covariate Adjustment
Assessment of Sensitivity Index
Sample Size Adjustment
Issues with Adaptive Randomization
Modifications of Hypotheses
Switch from Superiority to Noninferiority
Adaptive Dose-Escalation Trials
CRM in Phase I Oncology Study
Hybrid Frequentist-Bayesian Adaptive Design
Design Selection and Sample Size
Adaptive Group Sequential Design
General Approach for Group Sequential Design
Early Stopping Boundaries
Alpha Spending Function
Group Sequential Design Based on Independent P-Values
Calculation of Stopping Boundaries
Group Sequential Trial Monitoring
Statistical Tests for Seamless Adaptive Designs
Why a Seamless Design Is Efficient
Step-Wise Test and Adaptive Procedures
Contrast Test and Naive P-Value
Comparisons of Seamless Design
Drop-the-Loser Adaptive Design
Adaptive Sample Size Adjustment
Sample Size Re-Estimation without Unblinding Data
Generalization of Independent P-Value Approaches
Two-Stage Adaptive Design
Types of Two-Stage Adaptive Designs
Analysis for Seamless Design with Same Study Objectives/Endpoints
Analysis for Seamless Design with Different Endpoints
Analysis for Seamless Design with Different Objectives/Endpoints
Adaptive Treatment Switching
Latent Event Times
Proportional Hazard Model with Latent Hazard Rate
Mixed Exponential Model
Basic Concepts of Bayesian Approach
Multiple-Stage Design for Single-Arm Trial
Bayesian Optimal Adaptive Designs
Biomarker Adaptive Trials
Types of Biomarkers and Validation
Design with Classifier Biomarker
Adaptive Design with Prognostic Biomarker
Adaptive Design with Predictive Marker
Target Clinical Trials
Potential Impact and Significance
Evaluation of Treatment Effect
Other Study Designs and Models
Sample Size and Power Estimation
Framework and Model/Parameter Assumptions
Method Based on the Sum of P-Values
Method Based on Product of P-Values
Method with Inverse-Normal P-Values
Sample Size Re-Estimation
Clinical Trial Simulation
Software Application of ExpDesign Studio
Early Phases Development
Late Phases Development
Regulatory Perspectives — A Review of FDA Draft Guidance
The FDA Draft Guidance
Less Well-Understood Designs
Adaptive Design Implementation
Adaptive Group Sequential Design
Adaptive Dose-Escalation Design
Two-Stage Phase II/III Adaptive Design
Shein-Chung Chow is a professor in the Department of Biostatistics and Bioinformatics at Duke University School of Medicine. Dr. Chow is also an adjunct professor of clinical sciences at Duke–National University of Singapore Graduate Medical School and the editor-in-chief of the Journal of Biopharmaceutical Statistics. He has authored or co-authored numerous papers and books, including the Handbook of Adaptive Designs in Pharmaceutical and Clinical Development and Controversial Statistical Issues in Clinical Trials.
Mark Chang is the executive director of biostatistics and data management at AMAG Pharmaceuticals and an adjunct professor at Boston University. A fellow of the American Statistical Association, Dr. Chang is a co-founder of the International Society for Biopharmaceutical Statistics and serves on the editorial boards of two statistical journals. He has authored many publications, including Adaptive Design Theory and Implementation Using SAS and R and Monte Carlo Simulation for the Pharmaceutical Industry: Concepts, Algorithms, and Case Studies.