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.

翻译：多数临床试验都涉及将新治疗方法与控制装置(如护理标准)进行比较和估计治疗效果。外部数据,包括历史临床试验数据和现实世界观测数据,通常可供控制装置使用。从外部数据中借用信息有可能改进对相关参数的估计,如果存在的话,则增加检测治疗效果的能力。在本文中,我们提议利用巴耶西亚添加性回归树(BART)将外部数据纳入临床试验分析,具体目标是估计有条件或人口平均治疗效果。BART自然调整病人水平的同源体和捕捉不同数据来源之间可能的多种治疗效果,实现灵活的借用。模拟研究表明,BART优于等级线性模型和正常的等级模型。我们用针刺试验来说明拟议方法。