Variable selection for propensity score models
Variable selection for propensity score models
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DOI：
10.1093/aje/kwj149
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发表时间：
2006-06-15
影响因子：
5
通讯作者：
Sturmer, Til
中科院分区：
文献类型：
--
作者：
Brookhart, M. Alan;Schneeweiss, Sebastian;Sturmer, Til
尽管流行病学中的倾向评分（PS）方法越来越受欢迎，但在流行病学文献中关于PS模型变量选择问题的报道相对较少。作者提出了两个模拟研究的结果，旨在帮助流行病学家深入了解PS分析中的变量选择问题。模拟研究说明了PS模型中包含的变量的选择如何影响估计暴露效应的偏倚、方差和均方误差。结果表明，与暴露无关但与结果相关的变量应始终包含在PS模型中。纳入这些变量将降低估计暴露效应的方差，而不会增加偏倚。相反，包括与暴露相关但与结果无关的变量将增加估计暴露效应的方差，而不会减少偏倚。在非常小的研究中，纳入与暴露密切相关但与结果仅弱相关的变量可能不利于均方误差意义上的估计。这些变量的增加只消除了少量的偏差，但可以增加估计的暴露效应的方差。这些模拟研究和其他分析结果表明，标准的模型构建工具，旨在创建良好的预测模型的曝光并不总是导致最佳的PS模型，特别是在小型研究。
Despite the growing popularity of propensity score (PS) methods in epidemiology, relatively little has been written in the epidemiologic literature about the problem of variable selection for PS models. The authors present the results of two simulation studies designed to help epidemiologists gain insight into the variable selection problem in a PS analysis. The simulation studies illustrate how the choice of variables that are included in a PS model can affect the bias, variance, and mean squared error of an estimated exposure effect. The results suggest that variables that are unrelated to the exposure but related to the outcome should always be included in a PS model. The inclusion of these variables will decrease the variance of an estimated exposure effect without increasing bias. In contrast, including variables that are related to the exposure but not to the outcome will increase the variance of the estimated exposure effect without decreasing bias. In very small studies, the inclusion of variables that are strongly related to the exposure but only weakly related to the outcome can be detrimental to an estimate in a mean squared error sense. The addition of these variables removes only a small amount of bias but can increase the variance of the estimated exposure effect. These simulation studies and other analytical results suggest that standard model-building tools designed to create good predictive models of the exposure will not always lead to optimal PS models, particularly in small studies.