Houston Location Seminar: Self-adapting Mixture Prior to Dynamically Borrow Information from Historical Data in Clinical Trials

When: 1/23, 12 noon

Where: Houston, RAS E-101

Title: Self-adapting Mixture Prior to Dynamically Borrow Information from Historical Data in Clinical Trials

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. Moreover, SAM priors are easy to compute, data-driven, and calibration-free, mitigating the risk of data dredging. Numerical studies show that SAM priors outperform existing methods in adopting prior-data conflicts effectively. We developed R package ``SAMprior" and web application that are freely available at CRAN and \url{www.trialdesign.org} to facilitate the use of SAM priors.

Ying Yuan is Bettyann Asche Murray Distinguished Professor and Deputy Chair in the Department of Biostatistics at University of Texas MD Anderson Cancer Center. Dr. Yuan is an internationally renowned researcher in innovative Bayesian adaptive designs, with over 150 statistical methodology papers published on early phase trials, seamless trials, biomarker-guided trials, and basket and platform trials. The designs and software developed by Dr. Yuan’s lab (www.trialdesign.org) have been widely used in medical research institutes and pharmaceutical companies. The BOIN design, developed by Dr. Yuan’s team, is a groundbreaking oncology dose-finding design that has been recognized by the FDA as a fit-for-purpose drug development tool. Dr. Yuan was elected as the American Statistical Association Fellow, and is the leading author of two books, “Bayesian Designs for Phase I-II Clinical Trials” and “Model-Assisted Bayesian Designs for Dose Finding and Optimization,” both published by Chapman & Hall/CRC.

For those who can attend in person at our Houston location, we will provide a light lunch in RAS 101. I hope to see you there.

If you cannot attend in person, please use this WebEx link to attend: