Short Course 3: Bayesian Designs of Clinical Trials Using Historical Data: From Theory to Practice

May 18, 2025, 8:30am - 5:00pm, May 18, 2025

Stanford Campus, LKSC 120

Instructor: Ming-Hui Chen, Min Lin, and Yanyan Zhu, University of Connecticut

Dr. Ming-Hui Chen is a Board of Trustees Distinguished Professor and Head of Department of Statistics at University of Connecticut (UConn). He was elected to Fellow of American Association for the Advancement of Science (AAAS) in 2024, Fellow of International Society for Bayesian Analysis in 2016, Fellow of Institute of Mathematical Statistics in 2007, and Fellow of American Statistical Association in 2005. He received the University of Connecticut AAUP Research Excellence Award in 2013, the UConn College of Liberal Arts and Sciences (CLAS) Excellence in Research Award in the Physical Sciences Division in 2013, the University of Connecticut Alumni Association's University Award for Faculty Excellence in Research and Creativity (Sciences) in 2014, the ICSA Distinguished Achievement Award in 2020, and the Distinguished Science Alumni Award from Purdue University in 2023. He has published 460+ peer-reviewed journal articles and five books including two advanced graduate-level books on Bayesian survival analysis and Monte Carlo methods in Bayesian computation. He has supervised 42 PhD students. He served as, President of ICSA (2013), Chair of the Eastern Asia Chapter of International Society for Bayesian Analysis (2018), President of New England Statistical Society (2018-2020), and the 2022 JSM Program Chair. Currently, he is Co Editor-in-Chief of Statistics and Its Interface, inaugurated Co Editor-in-Chief of New England Journal of Statistics in Data Science, and an Associate Editor for several other statistical journals.

Min Lin is a fourth-year PhD student in Statistics at the University of Connecticut and a Research Fellow at Servier Pharmaceuticals. His research at Servier focuses on developing propensity-score-integrated Bayesian approaches for leveraging external control data. More broadly, his research interests include Bayesian clinical trial design and the development of new methods for effective sample size calculation to quantify the benefit-risk trade-off of external data borrowing. Additionally, he collaborates with the Connecticut Agricultural Experiment Station, providing statistical insights into invasive aquatic plant management at Candlewood Lake, with a focus on the effects of grass carp and winter drawdowns.

Yanyan Zhu is a fourth-year PhD student in Statistics at the University of Connecticut. She is holding a Vertex Fellowship, leading research projects on joint models of longitudinal and survival data. She is an active member of the Statistics in Pediatric Drug Development – Extrapolation Sub-team. Her research interests also include Bayesian design of clinical trials.

Abstract

Bayesian sample size determination (SSD) has a long history. The early work on Bayesian SSD can be traced back to 1990’s. Recently, several new methods on Bayesian designs of clinical trials has been developed with a focus on controlling type I error and power. In this short course, an overview of the literature on Bayesian SSD will be provided. The general theory and various methods of Bayesian SSD will be presented. The short course will also highlight several important applications in designing clinical trials to demonstrate the superiority of Bayesian SSD.

This short course starts with a brief review of early development of Bayesian SSD. Then, a comprehensive overview of the general framework of the Bayesian design will follow. Next, an in-depth exposition and discussion of the Bayesian design of a noninferiority trial for a medical device will be provided.

A key challenge in Bayesian SSD is determining the minimum required sample size to achieve a prespecified frequentist power, as Bayesian power calculations often depend on computationally intensive Monte Carlo sampling. We discuss methods to reduce computational burden and illustrate the methodology using a dedicated R package currently under development.

Pediatric extrapolation leverages existing data, often from adult or older pediatric populations to support drug development in children. This part will introduce a Bayesian framework that ensures the exact type I error control in the absence of borrowing. To determine the appropriate level of borrowing from the historical adult data, an analytical procedure will be presented to respect a pre-specified Type I error inflation threshold. The methodology will be demonstrated via an R Shiny app.

The intended audience for this course includes statisticians, data scientists, and clinical trial practitioners who hold at least a masters-level degree in statistics or a related field. The primary teaching objectives for this course are to (1) provide practitioners with a better understanding of basic concepts of Bayesian clinical trial design and sample size determination, (2) help practitioners understand the benefits and challenges of applying Bayesian methods in clinical trial designs, and (3) to demonstrate the implementation and evaluation of Bayesian designs through real applications and user-friendly interfaces such as R packages and R Shiny apps. By providing applied practitioners with a comprehensive understanding of important concepts related to Bayesian design of clinical trials, they will be much more equipped for appropriately using these new methods in real applications.