Join the Central Indiana Chapter for a virtual lunchtime presentation by Dr. Yong Zang of the IU Department of Biostatistics. This is a free event, but registration via Zoom is required.
Title: Bayesian Information Borrowing Prior for Longitudinal Data with Informative Dropout
Speaker: Dr. Yong Zang
Associate Professor
IU Department of Biostatistics
Date: Tuesday, February 10, 2026, Noon - 1:00 ET
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.
Speaker
Dr. Yong Zang is a Showalter Scholar at the IU School of Medicine in the Department of Biostatistics and Health Data Science. Yong earned his PhD in 2011 from University of Hong Kong and has been with the Department of Biostatistics since 2016. His research interests include the theoretical, algorithmic, and software development for adaptive clinical trial design and analysis, methods and testing for statistical genetics, and Bayesian analysis.