A Perspective Review of FDA's Draft Guidance on the Use of Bayesian Methodology in Clinical Trials скачать в хорошем качестве

A Perspective Review of FDA's Draft Guidance on the Use of Bayesian Methodology in Clinical Trials

Presentation Title: A Perspective Review of FDA's Draft Guidance on the Use of Bayesian Methodology in Clinical Trials of Drug and Biological Products Abstract: This review provides a foundational illustration of Bayesian philosophy as a logical framework for scientific inference, tracing its historical evolution, probability-theoretic underpinnings, and natural connection to human decision-making. I examine the FDA's January 2026 draft guidance, highlighting key provisions relevant to Bayesian methods in clinical trials, with particular emphasis on Phase 3 pivotal studies. Central concepts—including Type I error control, error rate calibration, multiplicity adjustment, and sequential decision-making—are discussed in the context of both calibrated and non-calibrated Bayesian designs. Future perspectives on optimizing Bayesian methods for clinical development are presented, including the practice of calibrating Bayesian decision rules against frequentist Type I error rates. Finally, practical recommendations are proposed for the appropriate application of Bayesian designs and success criteria in regulatory submissions approach improves the precision of treatment effect estimates while reducing the biases associated with borrowing data from external sources. Reference: Ji, Y., & D, Ph. (2026). Regulatory Expectations for Bayesian Methods in Drug and Biologic Clinical Trials: A Practical Perspective on FDA’s 2026 Draft Guidance (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2601.1.... Read More About the Speaker: Dr. Yuan Ji is Professor of Biostatistics at The University of Chicago. His research focuses on innovative Bayesian statistical methods for translational cancer research. Dr. Ji is author of over 200 publications in peer-reviewed journals including across medical and statistical journals. He is the inventor of many innovative Bayesian adaptive designs such as the mTPI and i3+3 designs, which have been widely applied in dose-finding clinical trials worldwide. His work on cancer genomics has been reported by a large number of media outlets in 2015. He received Mitchell Prize in 2015 by the International Society for Bayesian Analysis. He is an elected fellow of the American Statistical Association.