论文标题
在医疗保健数据库研究中多种治疗的因果影响估计罕见结果
Estimation of causal effects of multiple treatments in healthcare database studies with rare outcomes
论文作者
论文摘要
大规模的医疗保健数据库为比较有效性研究提供了丰富的机会。做出明智的治疗决定所需的证据通常依赖于比较多种治疗选择的有效性与少数个体中观察到的感兴趣的结果。通过多种疗法和罕见结果的因果推断是文献中很少处理的主题。本文设计了三套模拟，这是我们医疗保健数据库研究结构的代表，并提出了针对此类环境的因果分析策略。我们研究并比较了三种类型的方法及其变体的工作特性：贝叶斯添加剂回归树（BART），对广义倾向分数（RAMS）多变量样条的回归调整（RAMS）以及对治疗加权的逆概率（IPTW），具有多项式逻辑回归或普遍的增强模型。我们的结果表明，BART和RAM提供较低的偏差和平方误差，并且广泛使用的IPTW方法提供了不利的操作特性。我们使用一项案例研究说明了这些方法，该方法评估了机器人辅助手术，视频辅助胸腔镜外科手术和开放式胸腔切开术的比较有效性，以治疗非小细胞肺癌。
The preponderance of large-scale healthcare databases provide abundant opportunities for comparative effectiveness research. Evidence necessary to making informed treatment decisions often relies on comparing effectiveness of multiple treatment options on outcomes of interest observed in a small number of individuals. Causal inference with multiple treatments and rare outcomes is a subject that has been treated sparingly in the literature. This paper designs three sets of simulations, representative of the structure of our healthcare database study, and propose causal analysis strategies for such settings. We investigate and compare the operating characteristics of three types of methods and their variants: Bayesian Additive Regression Trees (BART), regression adjustment on multivariate spline of generalized propensity scores (RAMS) and inverse probability of treatment weighting (IPTW) with multinomial logistic regression or generalized boosted models. Our results suggest that BART and RAMS provide lower bias and mean squared error, and the widely used IPTW methods deliver unfavorable operating characteristics. We illustrate the methods using a case study evaluating the comparative effectiveness of robotic-assisted surgery, video-assisted thoracoscopic surgery and open thoracotomy for treating non-small cell lung cancer.
