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 vastly outperforms alternative methods. 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提供的证据来补充越来越昂贵和困难的随机控制试验(RCT)提供了补充机会;在本文件中,我们利用EHR数据构建合成控制武器,只进行单臂治疗试验;我们提出了一个新的非对称巴伊西亚常见原子混合模型,使我们能够在EHR和治疗臂中找到等同的人口层,然后再将EHR数据复制出来,以便在单一手臂试验和重新采样的EHR下创造等同的病人群体;通过无密度重要性取样办法进行抽查;然后利用合成控制装置推断治疗效果,可以使用RCT的任何可用方法进行;或者,拟议的非参数巴伊斯模式允许直接的模型推断;在模拟试验中,拟议的方法大大优于替代方法;我们采用基于历史试验的合成控制臂,用合成控制器补充单臂只进行治疗的血浆瘤研究的方法。