  Most clinical trials involve the comparison of a new treatment to a control
arm (e.g., the standard of care) and the estimation of a treatment effect.
External data, including historical clinical trial data and real-world
observational data, are commonly available for the control arm. Borrowing
information from external data holds the promise of improving the estimation of
relevant parameters and increasing the power of detecting a treatment effect if
it 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 average
treatment effect. BART naturally adjusts for patient-level covariates and
captures potentially heterogeneous treatment effects across different data
sources, achieving flexible borrowing. Simulation studies demonstrate that BART
compares favorably to a hierarchical linear model and a normal-normal
hierarchical model. We illustrate the proposed method with an acupuncture
trial.
