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Most clinical trials involve the comparison of a new treatment to a controlarm (e.g., the standard of care) and the estimation of a treatment effect.External data, including historical clinical trial data and real-worldobservational data, are commonly available for the control arm. Borrowinginformation from external data holds the promise of improving the estimation ofrelevant parameters and increasing the power of detecting a treatment effect ifit exists. In this paper, we propose to use Bayesian additive regression trees(BART) for incorporating external data into the analysis of clinical trials,with a specific goal of estimating the conditional or population averagetreatment effect. BART naturally adjusts for patient-level covariates andcaptures potentially heterogeneous treatment effects across different datasources, achieving flexible borrowing. Simulation studies demonstrate that BARTcompares favorably to a hierarchical linear model and a normal-normalhierarchical model. We illustrate the proposed method with an acupuncturetrial.

Incorporating External Data into the Analysis of Clinical Trials via Bayesian Additive Regression Trees