Department of Epidemiology, School of Public Health, University of California, Berkeley, Berkeley, California.
Departments of Mental Health, Biostatistics, and Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland.
Am J Epidemiol. 2018 Mar 1;187(3):604-613. doi: 10.1093/aje/kwx248.
Propensity score methods are a popular tool with which to control for confounding in observational data, but their bias-reduction properties-as well as internal validity, generally-are threatened by covariate measurement error. There are few easy-to-implement methods of correcting for such bias. In this paper, we describe and demonstrate how existing sensitivity analyses for unobserved confounding-propensity score calibration, VanderWeele and Arah's bias formulas, and Rosenbaum's sensitivity analysis-can be adapted to address this problem. In a simulation study, we examine the extent to which these sensitivity analyses can correct for several measurement error structures: classical, systematic differential, and heteroscedastic covariate measurement error. We then apply these approaches to address covariate measurement error in estimating the association between depression and weight gain in a cohort of adults in Baltimore, Maryland. We recommend the use of VanderWeele and Arah's bias formulas and propensity score calibration (assuming it is adapted appropriately for the measurement error structure), as both approaches perform well for a variety of propensity score estimators and measurement error structures.
倾向评分法是一种在观察性数据中控制混杂因素的常用工具，但由于协变量测量误差，其偏倚减少特性以及内部有效性通常受到威胁。很少有易于实施的方法可以纠正这种偏差。在本文中，我们描述并展示了如何适应现有的未观察到混杂因素的敏感性分析-倾向评分校准，VanderWeele 和 Arah 的偏差公式以及Rosenbaum 的敏感性分析-来解决此问题。在一项模拟研究中，我们研究了这些敏感性分析在纠正几种测量误差结构方面的程度：经典，系统差异和异方差协变量测量误差。然后，我们将这些方法应用于解决马里兰州巴尔的摩市成年人队列中抑郁与体重增加之间关联的协变量测量误差问题。我们建议使用 VanderWeele 和 Arah 的偏差公式和倾向评分校准（假设它适当地适应了测量误差结构），因为这两种方法对于各种倾向评分估计和测量误差结构都表现良好。
