Lin D Y, Psaty B M, Kronmal R A
Department of Biostatistics, University of Washington, Seattle 98195, USA.
Biometrics. 1998 Sep;54(3):948-63.
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 variable 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 on two major medical studies.
本文提出了一种通用方法，用于评估观察性研究中主要暴露效应的点估计和区间估计对于在调整了测量协变量后未测量变量的残余混杂效应的敏感性。所提出的方法假设真实暴露效应可以在一个回归模型中表示，该模型包括暴露指标以及已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以及未测量混杂因素对结局变量的影响，人们可以使用省略未测量混杂因素的相应简化模型来对真实暴露效应进行统计推断。在某些条件下，完整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报告中获得的表观暴露效应的点估计和区间估计进行简单调整，来估计真实暴露效应。所提出的方法可处理二元响应和删失生存时间数据，适用于任何研究设计，并允许未测量的混杂因素为离散分布或正态分布。我们描述了在两项主要医学研究中的应用。
