Department of Epidemiology and Preventive Medicine, Monash University, Australia.
Stat Med. 2012 Dec 30;31(30):4382-400. doi: 10.1002/sim.5643. Epub 2012 Oct 22.
We consider the estimation of the causal effect of a binary exposure on a continuous outcome. Confounding and missing data are both likely to occur in practice when observational data are used to estimate this causal effect. In dealing with each of these problems, model misspecification is likely to introduce bias. We present augmented inverse probability weighted (AIPW) estimators that account for both confounding and missing data, with the latter occurring in a single variable only. These estimators have an element of robustness to misspecification of the models used. Our estimators require two models to be specified to deal with confounding and two to deal with missing data. Only one of each of these models needs to be correctly specified. When either the outcome or the exposure of interest is missing, we derive explicit expressions for the AIPW estimator. When a confounder is missing, explicit derivation is complex, so we use a simple algorithm, which can be applied using standard statistical software, to obtain an approximation to the AIPW estimator.
我们考虑估计二元暴露对连续结果的因果效应。在使用观察数据估计这种因果效应时，混杂和数据缺失在实践中都很可能发生。在处理这两个问题时，模型误设定都可能引入偏差。我们提出了增强逆概率加权（AIPW）估计量，它同时考虑了混杂和数据缺失，后者仅在一个变量中发生。这些估计量对于所使用模型的误设定具有一定的稳健性。我们的估计量需要指定两个模型来处理混杂，两个模型来处理缺失数据。每个模型只需要正确指定一个。当感兴趣的结局或暴露缺失时，我们推导出 AIPW 估计量的显式表达式。当混杂因子缺失时，显式推导很复杂，因此我们使用一个简单的算法，该算法可以使用标准统计软件应用，以获得 AIPW 估计量的近似值。
