
Group Sequential and Adaptive Methods for Clinical Trials
Description
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Features
Eleven entirely new chapters, including optimal design construction, delayed "pipeline" data accommodation, and multiple comparison procedures
Eight dedicated chapters on adaptive methods including sample size re-assessment, seamless Phase II/III trials, multi-arm multi-stage trials, and enrichment designs
Exact methods for binary and Poisson data with applications to vaccine trials
Expanded discussion of binding versus non-binding futility boundaries for enhanced decision-making
A chapter with a thoroughly updated review of Bayesian approaches to sequential trial design and analysis
Incorporates response-adaptive treatment assignment in group sequential designs
Practical computational methods and software guidance for implementing the techniques
This definitive resource serves statisticians, clinical researchers, pharmaceutical scientists, and regulatory professionals involved in clinical trial design and analysis. The text balances theoretical foundations with practical implementation guidance, making it invaluable for both experienced practitioners and those new to adaptive trial methodology. With twice the content of the first edition, Group Sequential and Adaptive Methods for Clinical Trials, Second Edition provides the comprehensive understanding needed to design more efficient, ethical, and scientifically rigorous clinical trials, ultimately accelerating the delivery of effective treatments to patients.
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Persons
Bruce Turnbull is Emeritus Professor of Statistics in the School of Operations Research & Industrial Engineering and in the Department of Statistical Science, Cornell University, USA. He has a long experience of designing, monitoring, and analyzing clinical trials and has served on the Data Safety Monitoring Boards for international, multi-center trials for the treatment or prevention of cancer, heart disease and AIDS. He has also served as a consultant to pharmaceutical companies and health-related government review panels.
Content
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