1st Edition

Handbook of Adaptive Designs in Pharmaceutical and Clinical Development

Edited By Annpey Pong, Shein-Chung Chow Copyright 2010
    496 Pages
    by CRC Press

    496 Pages 60 B/W Illustrations
    by CRC Press

    In response to the US FDA’s Critical Path Initiative, innovative adaptive designs are being used more and more in clinical trials due to their flexibility and efficiency, especially during early phase development. Handbook of Adaptive Designs in Pharmaceutical and Clinical Development provides a comprehensive and unified presentation of the principles and latest statistical methodologies used when modifying trial procedures based on accrued data of ongoing clinical trials. The book also gives a well-balanced summary of current regulatory perspectives.

    The first several chapters focus on the fundamental theory behind adaptive trial design, the application of the Bayesian approach to adaptive designs, and the impact of potential population shift due to protocol amendments. The book then presents a variety of statistical methods for group sequential design, classical design, dose-finding trials, Phase I/II and Phase II/III seamless adaptive designs, multiple stage seamless adaptive trial design, adaptive randomization trials, hypotheses-adaptive design, and treatment-adaptive design. It also covers predictive biomarker diagnostics for new drug development, clinical strategies for endpoint selection in translational research, the role of independent data monitoring committees in adaptive clinical trials, the enrichment process in targeted clinical trials for personalized medicine, applications of adaptive designs that use genomic or genetic information, adaptive trial simulation, and the efficiency of adaptive design. The final chapters discuss case studies as well as standard operating procedures for good adaptive practices.

    With contributions from leading clinical researchers in the pharmaceutical industry, academia, and regulatory agencies, this handbook offers an up-to-date, complete treatment of the principles and methods of adaptive design and analysis. Along with reviewing recent developments, it examines issues commonly encountered when applying adaptive design methods in clinical trials.

    Overview of Adaptive Design Methods in Clinical Trials. Fundamental Theory of Adaptive Designs with Unplanned Design Change in Clinical Trials with Blinded Data. Bayesian Approach for Adaptive Design. The Impact of Protocol Amendments in Adaptive Trial Designs. From Group Sequential to Adaptive Designs. Determining Sample Size for Classical Designs. Sample Size Reestimation Design with Applications in Clinical Trials. Adaptive Interim Analyses in Clinical Trials. Classical Dose-Finding Trial. Improving Dose-Finding: A Philosophic View. Adaptive Dose-Ranging Studies. Seamless Phase I/II Designs. Phase II/III Seamless Designs. Sample Size Estimation/Allocation for Two-Stage Seamless Adaptive Trial Designs. Optimal Response-Adaptive Randomization for Clinical Trials. Hypothesis-Adaptive Design. Treatment Adaptive Allocations in Randomized Clinical Trials: An Overview. Integration of Predictive Biomarker Diagnostics into Clinical Trials for New Drug Development. Clinical Strategy for Study Endpoint Selection. Adaptive Infrastructure. Independent Data Monitoring Committees. Targeted Clinical Trials. Functional Genome-Wide Association Studies of Longitudinal Traits. Adaptive Trial Simulation. Efficiency of Adaptive Designs. Cases Studies in Adaptive Design. Good Practices for Adaptive Clinical Trials. Index.


    Annpey Pong is a manager in the Department of Biostatistics and Research Decision Sciences at Merck Research Laboratories. Dr. Pong is also the associate editor of the Journal of Biopharmaceutical Statistics. She earned her Ph.D. in statistics from Temple University.

    Shein-Chung Chow is a professor in the Department of Biostatistics and Bioinformatics at Duke University School of Medicine. Dr. Chow is also a professor of clinical sciences at Duke–National University of Singapore Graduate Medical School and the editor of the Journal of Biopharmaceutical Statistics. He earned his Ph.D. in statistics from the University of Wisconsin–Madison.