The availability of electronic health records (EHR) has opened opportunities to supplement increasingly expensive and difficult to carry out randomized controlled trials (RCT) with evidence from readily available real world data. In this paper, we use EHR data to construct synthetic control arms for treatment-only single arm trials. We propose a novel nonparametric Bayesian common atoms mixture model that allows us to find equivalent population strata in the EHR and the treatment arm and then resample the EHR data to create equivalent patient populations under both the single arm trial and the resampled EHR. Resampling is implemented via a density-free importance sampling scheme. Using the synthetic control arm, inference for the treatment effect can then be carried out using any method available for RCTs. Alternatively the proposed nonparametric Bayesian model allows straightforward model-based inference. In simulation experiments, the proposed method exhibits higher power than alternative methods in detecting treatment effects, specifically for non-linear response functions. We apply the method to supplement single arm treatment-only glioblastoma studies with a synthetic control arm based on historical trials.

翻译：电子健康记录（EHR）的可用性为利用现成真实世界数据补充日益昂贵且难以实施的随机对照试验（RCT）提供了机遇。本文利用EHR数据为仅含治疗组的单臂试验构建合成对照组。我们提出一种新颖的非参数贝叶斯公共原子混合模型，该模型能够识别EHR与治疗组中的等效人群分层，并通过重采样EHR数据在单臂试验与重采样EHR中创建等效患者群体。重采样通过无密度重要性采样方案实现。利用合成对照组，可采用RCT中任意可用方法对治疗效果进行推断；此外，所提出的非参数贝叶斯模型也允许直接基于模型的推断。仿真实验表明，该方法在检测治疗效果（尤其是非线性响应函数场景）时具有比替代方法更高的统计功效。我们将该方法应用于基于历史试验构建的合成对照组以补充仅含治疗组的单臂胶质母细胞瘤研究。