涩里番

Special Seminar: Monday, November 24, 2025,听from 3:30 to 4:30 pm in Room 1203

Ying Yuan, PhD

Bettyann Asche Murray Distinguished Professor
Chair of the Department of Biostatistics |
MD Anderson Cancer Center, University of Texas

WHEN: Monday, November 24, 2025, from 3:30 to 4:30 p.m.
WHERE: Hybrid | 2001 涩里番 College Avenue, Rm 1203;
NOTE:听Ying Yuan will be presenting in-person at SPGH听
听

Abstract

Mixture priors provide an intuitive way to incorporate historical data while accounting for potential prior-data conflict by combining an informative prior with a non-informative prior. However, pre-specifying the mixing weight for each component remains a crucial challenge. Ideally, the mixing weight should reflect the degree of prior-data conflict, which is often unknown beforehand, posing a significant obstacle to the application and acceptance of mixture priors. To address this challenge, we introduce self-adapting mixture (SAM) priors that determine the mixing weight using likelihood ratio test statistics or Bayes factors. SAM priors are data-driven and self-adapting, favoring the informative (non-informative) prior component when there is little (substantial) evidence of prior-data conflict. Consequently, SAM priors achieve dynamic information borrowing. We demonstrate that SAM priors exhibit desirable properties in both finite and large samples and achieve information-borrowing consistency. We developed R package "SAMprior" to facilitate the use of SAM priors.

Speaker Bio

Ying Yuan is the Bettyann Asche Murray Distinguished Professor and Chair of the Department of Biostatistics at the University of Texas MD Anderson Cancer Center. Dr. Yuan is internationally renowned for his pioneering research in innovative Bayesian adaptive designs, including early-phase trials, seamless trials, biomarker-guided trials, and basket and platform trials. The designs and software developed by Dr. Yuan鈥檚 lab () have been widely adopted by medical research institutes and pharmaceutical companies. Among these, the BOIN design, developed by Dr. Yuan鈥檚 team, is a groundbreaking oncology dose-finding method recognized by the FDA as a fit-for-purpose drug development tool. Dr. Yuan is also an elected Fellow of the American Statistical Association and the lead author of two seminal books:听Bayesian Designs for Phase I-II Clinical Trials听and听Model-Assisted Bayesian Designs for Dose Finding and Optimization, both published by Chapman & Hall/CRC.