论文标题
在使用反概率加权治疗的治疗效果方面的差异
On variance of the treatment effect in the treated using inverse probability weighting
论文作者
论文摘要
在观察性研究的分析中，通常使用反比概率加权（IPW）来始终如一地估计平均治疗效果（ATE）或治疗（ATT）的平均治疗效果。 IPW ATE估计量的方差通常是通过假设权重知道的，然后使用所谓的“鲁棒”（Huber-White）三明治估计量来估计的，这导致保守的标准误差（SE）估计。这里表明，在估计IPW ATT估计量的方差时使用这种方法并不一定会导致保守的SE估计值。也就是说，假设重量是已知的，那么强大的三明治估计量可能是保守的或抗保守的。因此，使用鲁棒SE估计的ATT的置信区间通常无效。取而代之的是，可以使用重量估计的堆叠估计方程式来计算IPW ATT估算器的一致，封闭形式的方差估计器。通过模拟研究和对吸烟对基因表达的影响的数据分析进行比较。
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 the weights are known and then using the so-called "robust" (Huber-White) sandwich estimator, which results in conservative standard error (SE) estimation. Here it is shown 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 conservative or anti-conservative. Thus confidence intervals of 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 two variance estimators are compared via simulation studies and in a data analysis of the effect of smoking on gene expression.
