The Successes and Challenges of Bayesian Adaptive Basket and Platform Trials in Practice: Lessons Learned

Speaker: J. Jack Lee, PhD, Professor, Department of Biostatistics, The University of Texas MD Anderson Cancer Center (Email)

Biography

J. Jack Lee, PhD, MS, DDS, is Professor of Biostatistics and the John G. & Marie Stella Kenedy Foundation Chair in Cancer Research at The University of Texas MD Anderson Cancer Center. He also directs MD Anderson's Biostatistics Resource Group. His research focuses on the design and analysis of cancer clinical trials, including Bayesian adaptive designs, statistical methods for precision medicine, drug-combination studies, and biomarker identification and validation. During more than 30 years at MD Anderson, Dr. Lee has collaborated with multidisciplinary teams across clinical, translational, and basic science research. He is a Fellow of the American Statistical Association, the Society for Clinical Trials, and the American Association for the Advancement of Science. He has published extensively in statistical and medical journals and co-authored books on Bayesian adaptive clinical-trial methods and model-assisted Bayesian dose-finding and optimization designs.

Abstract

Bayesian adaptive designs, including basket and platform trials, are master-protocol clinical trials designed to evaluate one or more interventions, disease subtypes, treatment combinations, or biomarker-defined cohorts within a single infrastructure. They use prospectively planned adaptations based on accumulating data and can run perpetually. Prominent features include shared control groups, response-adaptive randomization, interim decision-making, dropping toxic or ineffective arms, adding new arms, and seamless transitions across trial stages. Compared with conventional stand-alone randomized trials, adaptive platform trials can improve efficiency, accelerate learning, reduce duplication of infrastructure, and increase the probability that patients receive promising therapies. However, they also introduce operational, regulatory, statistical, and logistical complexities, including control of type I error, non-concurrent controls, delayed outcomes, multiplicity, changing standards of care, data-sharing governance, and the need for rapid, reliable decision-making based on appropriate endpoints. Prominent examples of adaptive platform trials include NCI-MATCH, I-SPY 2, and GBM AGILE. In this talk, key statistical innovations and lessons learned will be discussed. Despite their promise, successful implementation requires careful planning, extensive simulation of operating characteristics, robust infrastructure, clear governance, timely endpoint ascertainment, and close collaboration among statisticians, clinicians, sponsors, regulators, and data monitoring committees.

Keywords

Master protocol; Bayesian inference; efficient drug development; information borrowing; seamless design; simulations; operating characteristics