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Tuesday, January 7
Tue, Jan 7, 11:00 AM - 12:45 PM
Pacific C
Leveraging Real-World Data Using Novel Statistical Approaches for Regulatory Decision-making

A Bayesian Non-Parametric Causal Inference Model for Synthesizing Randomized Clinical Trial and Real World Evidence (306633)

Gary L. Rosner, Johns Hopkins University 
*Chenguang Wang, Johns Hopkins University 

Keywords: Bayesian Non-Parametric, Propensity Score, RCT, RWE

With the wide availability of various real-world data (RWD), there is an increasing interest in synthesizing information from both randomized clinical trials and RWD for healthcare decision makings. The task of addressing study-specific heterogeneities is one of the most difficult challenges in synthesizing data from disparate sources. Bayesian hierarchical model with non-parametric extension provide a powerful and convenient platform that formalizes the information borrowing strength across the studies. In this paper, we propose a propensity score-based Bayesian non-parametric Dirichlet process mixture model that summarizes subject-level information from randomized and registry studies to draw inference on the causal treatment effect. Simulation studies are conducted to evaluate the model performance of the model under different scenarios. In addition, we demonstrate the proposed method using data from a clinical study on angiotensin converting enzyme inhibitor for treating congestive heart failure.