The use of real-world data for medical treatment evaluation frequently requires adjusting for population differences. We consider this problem in the context of estimating mean outcomes and treatment differences in a well-defined target population, using clinical data from a study population that overlaps with but differs from the target population in terms of patient characteristics. The current literature on this subject includes a variety of statistical methods, which generally require correct specification of at least one parametric regression model. In this article, we propose to use machine learning methods to estimate nuisance functions and incorporate the machine learning estimates into existing doubly robust estimators. This leads to nonparametric estimators that are $\sqrt{n}$-consistent, asymptotically normal and asymptotically efficient under general conditions. Simulation results demonstrate that the proposed methods perform reasonably well in realistic settings. The methods are illustrated with a cardiology example concerning aortic stenosis.

中文翻译：

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使用机器学习方法调整人口差异

使用真实世界数据进行医疗评估通常需要针对人口差异进行调整。我们在估计明确定义的目标人群的平均结果和治疗差异的背景下考虑这个问题，使用来自研究人群的临床数据，这些数据与目标人群在患者特征方面重叠但与目标人群不同。当前关于该主题的文献包括多种统计方法，这些方法通常需要正确指定至少一个参数回归模型。在本文中，我们建议使用机器学习方法来估计滋扰函数，并将机器学习估计合并到现有的双鲁棒估计器中。这导致非参数估计量是$\sqrt{n}$- 在一般条件下一致、渐近正态和渐近有效。仿真结果表明，所提出的方法在现实环境中表现得相当好。这些方法用一个关于主动脉瓣狭窄的心脏病学例子来说明。