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Activity Number: 536 - Advanced Bayesian Methods for Modern Clinical Trials
Type: Invited
Date/Time: Thursday, August 11, 2022 : 10:30 AM to 12:20 PM
Sponsor: International Society for Bayesian Analysis (ISBA)
Abstract #320687
Title: Bayesian Nonparametric Common Atoms Regression for Generating Synthetic Controls in Clinical Trials
Author(s): Peter Mueller* and Noirrit Kiran Chandra and Abhra Sarkar
Companies: The University of Texas at Austin and The University of Texas at Austin and The University of Texas at Austin
Keywords: real world data; nonparametric Bayes; clinical trial

We develop a Bayesian nonparametric approach for creating synthetic controls from real world data (RWD) to supplement treatment-only single arm trials. We introduce a Bayesian common atoms regression model that clusters covariates with similar values across different treatment arms. Exploiting the common atoms structure, we propose a density free importance sampling scheme to sample a subpopulation of the RWD such that the covariates in the subpopulation have the same distribution as the actual patients, allowing for a valid treatment comparison. Inference under the proposed common atoms mixture model can be characterized as a stochastic stratification by propensity score (for selection into control or treatment arm). The proposed design is implemented for glioblastoma trials.

Authors who are presenting talks have a * after their name.

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