A Bayesian nonparametric causal inference model for synthesizing randomized clinical trial and real-world evidence.

Abstract

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 health-care decision makings. The task of addressing study-specific heterogeneities is one of the most difficult challenges in synthesizing data from disparate sources. Bayesian hierarchical models with nonparametric extension provide a powerful and convenient platform that formalizes the information borrowing strength across the sources. In this paper, we propose a propensity score-based Bayesian nonparametric 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 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.