Observational cohort studies are increasingly being used for comparative effectiveness research to assess the safety of therapeutics. Recently, various doubly robust methods have been proposed for average treatment effect estimation by combining the treatment model and the outcome model via different vehicles, such as matching, weighting, and regression. The key advantage of doubly robust estimators is that they require either the treatment model or the outcome model to be correctly specified to obtain a consistent estimator of average treatment effects, and therefore lead to a more accurate and often more precise inference. However, little work has been done to understand how doubly robust estimators differ due to their unique strategies of using the treatment and outcome models and how machine learning techniques can be combined to boost their performance. Here we examine multiple popular doubly robust methods and compare their performance using different treatment and outcome modeling via extensive simulations and a real-world application. We found that incorporating machine learning with doubly robust estimators such as the targeted maximum likelihood estimator gives the best overall performance. Practical guidance on how to apply doubly robust estimators is provided.

翻译：观察性队列研究越来越多地被用于比较有效性研究，以评估治疗措施的安全性。近年来，通过匹配、加权和回归等不同途径结合治疗模型与结局模型，多种双稳健方法被提出用于平均治疗效应估计。双稳健估计量的关键优势在于，只需治疗模型或结局模型其中之一正确设定，即可获得平均治疗效应的一致估计量，从而得出更准确且通常更精确的推断。然而，目前鲜有研究探讨不同双稳健估计量因使用治疗模型和结局模型的独特策略而产生的差异，以及如何结合机器学习技术提升其性能。本文通过大量模拟和一项真实世界应用，检验了多种主流双稳健方法，并比较了它们采用不同治疗模型和结局建模时的表现。研究发现，将机器学习与靶向最大似然估计等双稳健估计量相结合，可获得最佳整体性能。本文还提供了如何应用双稳健估计量的实践指导。