On a closed-form doubly robust estimator of the adjusted odds ratio for a binary exposure.
On a closed-form doubly robust estimator of the adjusted odds ratio for a binary exposure.
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基于二元风险调整优势比的封闭式双稳健估计器。
DOI：
10.1093/aje/kws377
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发表时间：
2013
影响因子：
5
通讯作者：
TchetgenTchetgen,EricJ
中科院分区：
文献类型：
--
作者：
TchetgenTchetgen,EricJ
流行病学研究通常旨在估计二元暴露与二元疾病结局之间关联的比值比。由于混杂偏倚在观察性研究中是一个严重的问题，研究者通常在多变量logistic回归中估计调整后的比值比，该回归以大量潜在混杂因素为条件。众所周知，混杂因素规范中的建模错误可导致暴露的调整优势比出现实质性偏倚。作为补救措施，Tchetgen Tchetgen等人（Biometrika.二○一○年; 97（1）：171-180）最近通过仔细地将标准逻辑回归与反向回归分析相结合，开发了所谓的调整优势比的双重稳健估计，其中暴露是因变量，结果和混杂因素都是自变量。双重稳健性意味着2种建模策略中仅需要一种是正确的，以便对比值比参数进行有效推断。在本文中，我的目的是介绍这种最新的方法到流行病学文献中，提出了一个简单的封闭形式的双稳健估计的调整后的比值比的二进制曝光。SAS宏（SAS Institute Inc.，卡里，北卡罗来纳州）的在线附录中给出，以方便在常规流行病学实践中使用该方法，并提供了一个模拟数据示例以供说明。
Epidemiologic studies often aim to estimate the odds ratio for the association between a binary exposure and a binary disease outcome. Because confounding bias is of serious concern in observational studies, investigators typically estimate the adjusted odds ratio in a multivariate logistic regression which conditions on a large number of potential confounders. It is well known that modeling error in specification of the confounders can lead to substantial bias in the adjusted odds ratio for exposure. As a remedy, Tchetgen Tchetgen et al. (Biometrika. 2010;97(1):171–180) recently developed so-called doubly robust estimators of an adjusted odds ratio by carefully combining standard logistic regression with reverse regression analysis, in which exposure is the dependent variable and both the outcome and the confounders are the independent variables. Double robustness implies that only one of the 2 modeling strategies needs to be correct in order to make valid inferences about the odds ratio parameter. In this paper, I aim to introduce this recent methodology into the epidemiologic literature by presenting a simple closed-form doubly robust estimator of the adjusted odds ratio for a binary exposure. A SAS macro (SAS Institute Inc., Cary, North Carolina) is given in an online appendix to facilitate use of the approach in routine epidemiologic practice, and a simulated data example is also provided for the purpose of illustration.