Reifeis Sarah A, Hudgens Michael G
Am J Epidemiol. 2022 May 20;191(6):1092-1097. doi: 10.1093/aje/kwac014.
In the analysis of observational studies, inverse probability weighting (IPW) is commonly used to consistently estimate the average treatment effect (ATE) or the average treatment effect in the treated (ATT). The variance of the IPW ATE estimator is often estimated by assuming that the weights are known and then using the so-called "robust" (Huber-White) sandwich estimator, which results in conservative standard errors (SEs). Here we show that using such an approach when estimating the variance of the IPW ATT estimator does not necessarily result in conservative SE estimates. That is, assuming the weights are known, the robust sandwich estimator may be either conservative or anticonservative. Thus, confidence intervals for the ATT using the robust SE estimate will not be valid, in general. Instead, stacked estimating equations which account for the weight estimation can be used to compute a consistent, closed-form variance estimator for the IPW ATT estimator. The 2 variance estimators are compared via simulation studies and in a data analysis of the association between smoking and gene expression.
在观察性研究的分析中，逆概率加权法（IPW）通常用于一致地估计平均治疗效果（ATE）或治疗组中的平均治疗效果（ATT）。IPW ATE估计量的方差通常通过假设权重已知，然后使用所谓的“稳健”（Huber-White）三明治估计量来估计，这会导致标准误（SE）保守。在这里，我们表明，在估计IPW ATT估计量的方差时使用这种方法不一定会导致保守的SE估计。也就是说，假设权重已知，稳健三明治估计量可能是保守的，也可能是反保守的。因此，一般来说，使用稳健SE估计的ATT置信区间是无效的。相反，可以使用考虑权重估计的堆叠估计方程来计算IPW ATT估计量的一致、封闭形式的方差估计量。通过模拟研究和对吸烟与基因表达之间关联的数据分析，对这两种方差估计量进行了比较。
