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

arXiv: https://arxiv.org/abs/2103.08754 · HTML: https://mlsanity.com/paper/2103.08754/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2021.

## Built on by (most cited first, 2 of 2)

- [A Causal Inference Framework for Leveraging External Controls in Hybrid Trials](https://mlsanity.com/paper/2305.08969/) (2023)
- [Bayesian fusion forests for heterogeneous treatment effects on survival from randomised and real-world data](https://mlsanity.com/paper/2607.29295/) (2026)

## Abstract (arXiv)

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.