Propensity Score-Based Estimators With Multiple Error-Prone Covariates.
Propensity Score-Based Estimators With Multiple Error-Prone Covariates.
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具有多个易错协变量的基于倾向评分的估计器。
DOI：
10.1093/aje/kwy210
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发表时间：
2019
影响因子：
5
通讯作者：
Stuart,ElizabethA
中科院分区：
文献类型：
--
作者：
Hong,Hwanhee;Aaby,DavidA;Siddique,Juned;Stuart,ElizabethA
倾向评分法是帮助减少非实验研究中混淆的重要工具。大多数倾向评分方法都假定协变量的测量没有误差。然而，协变量的测量经常有误差，如果真正的潜在协变量是实际的混杂因素，这将导致有偏差的因果效应估计。尽管一些研究小组已经研究了单个协变量对估计因果效应的影响，并提出了处理测量误差的方法，但很少有人研究了多个协变量被错误测量的情况，而且我们没有发现任何讨论相关测量误差的研究。在这项研究中，我们通过广泛的模拟研究和实际数据分析，使用基于倾向分数的估计器估计因果效应时，检查了多个容易出错的协变量的后果。我们发现，当倾向得分模型包含误测的协变量时，因果效应估计的偏差较小，这些协变量的真实潜在值彼此之间存在强相关性。然而，当测量误差相互关联时，会引入额外的偏差。此外，包含正确测量的辅助变量是有益的，这些辅助变量与混杂因素相关，这些混杂因素的真实潜在值在倾向评分模型中被错误测量。
Propensity score methods are an important tool to help reduce confounding in nonexperimental studies. Most propensity score methods assume that covariates are measured without error. However, covariates are often measured with error, which leads to biased causal effect estimates if the true underlying covariates are the actual confounders. Although some groups have investigated the impact of a single mismeasured covariate on estimating a causal effect and proposed methods for handling the measurement error, fewer have investigated the case where multiple covariates are mismeasured, and we found none that discussed correlated measurement errors. In this study, we examined the consequences of multiple error-prone covariates when estimating causal effects using propensity score–based estimators via extensive simulation studies and real data analyses. We found that causal effect estimates are less biased when the propensity score model includes mismeasured covariates whose true underlying values are strongly correlated with each other. However, when the measurement errors are correlated with each other, additional bias is introduced. In addition, it is beneficial to include correctly measured auxiliary variables that are correlated with confounders whose true underlying values are mismeasured in the propensity score model.