Assessing the sensitivity of regression results to unmeasured confounders in observational studies
Assessing the sensitivity of regression results to unmeasured confounders in observational studies
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DOI：
10.2307/2533848
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发表时间：
1998-09-01
期刊：
影响因子：
1.9
通讯作者：
Kronmal, RA
中科院分区：
文献类型：
--
作者：
Lin, DY;Psaty, BM;Kronmal, RA
本文提出了一种评估观察性研究中主要暴露效应的点估计和区间估计对未测量变量的剩余混杂效应的敏感性的一般方法。所提出的方法假设，真实的暴露效果可以表示在一个回归模型，其中包括暴露指标以及测量和未测量的混杂因素。通过指定暴露组和未暴露组中未测量混杂因素的分布沿着未测量混杂因素对结果变量的影响，可以使用相应的简化模型（省略未测量混杂因素）对真实暴露效应进行统计推断。在一定条件下，完全模型中的真实暴露效应与简化模型中的表观暴露效应之间存在简单的代数关系。然后，人们可以通过对从标准软件或已发表的报告中获得的表观暴露效应的点和区间估计值进行简单调整来估计真实的暴露效应。所提出的方法处理二进制响应和删失生存时间数据，适应任何研究设计，并允许未测量的混杂因素是离散的或正态分布的。我们描述了两个主要的医学研究的应用。
This paper presents a general approach for assessing the sensitivity of the point and interval estimates of the primary exposure effect in an observational study to the residual confounding effects of unmeasured variables after adjusting for measured covariates. The proposed method assumes that the true exposure effect can be represented in a regression model that includes the exposure indicator as well as the measured and unmeasured confounders. One can use the corresponding reduced model that omits the unmeasured confounder to make statistical inferences about the true exposure effect by specifying the distributions of the unmeasured confounder in the exposed and unexposed groups along with the effects of the unmeasured confounder on the outcome variable. Under certain conditions, there exists a simple algebraic relationship between the true exposure effect in the full model and the apparent exposure effect in the reduced model. One can then estimate the true exposure effect by making a simple adjustment to the point and interval estimates of the apparent exposure effect obtained from standard software or published reports. The proposed method handles both binary response and censored survival time data, accommodates any study design, and allows the unmeasured confounder to be discrete or normally distributed. We describe applications to two major medical studies.