January 8, 2026 Webinar
Bayesian Information Borrowing Prior for Longitudinal Data with Informative Dropout
Yong Zang, PhD
Abstract:
Borrowing information from historical controls can improve the efficiency of randomized controlled trials (RCTs), but its application to longitudinal outcomes with informative dropout remains limited. We propose two Bayesian mixture priors for longitudinal data information borrowing: the mixture prior for longitudinal data borrowing (MLB) and its self-adapting extension (SLB). Both approaches use a shared-parameter model to handle the informative dropout and apply a mixture prior framework to incorporate historical control data while accounting for possible prior-data conflict. Simulation studies show that the proposed priors yield desirable operating characteristics enabling efficient and rigorous information borrowing. In particular, the SLB prior demonstrates the best overall performance.
Short Bio:
Dr Yong Zang is the Indiana University School of Medicine Showalter Scholar Associate Professor in the Department of Biostatistics and Health Data Science, Indiana University. He also serves as the Co-Director of Clinical Research for the Biostatistics and Data Management Core, IU Simon Comprehensive Cancer Center. He received his Ph.D. degree in Statistics from the University of Hong Kong and finished his Postdoctoral training in The University of Texas, MD Anderson Cancer Center. His research interests are clinical trial design and health data informatics. He has published over eighty papers in peer-reviewed statistical, informatics and medical journals. His research is supported by National Institute of Health, Showalter Trust, Indiana CTSI and Eli Lilly.
Hope to see you at the webinar!