Utilizing stratified generalized propensity score matching to approximate blocked trial designs with multiple treatment levels
Utilizing stratified generalized propensity score matching to approximate blocked trial designs with multiple treatment levels
复制标题
利用分层广义倾向评分匹配来近似具有多个治疗水平的封闭试验设计
DOI：
10.1080/10543406.2.22.2065507
复制
发表时间：
2022
影响因子：
1.1
通讯作者：
Yang, Shu
中科院分区：
文献类型：
--
作者：
Corder, Nathan;Yang, Shu
观测数据的缺失问题是普遍存在的。当混杂因素随机缺失时，通常使用多重插补;然而，该方法要求有效推断的同类性条件，在估计平均因果治疗效应时可能无法满足。或者，Kim 2011年提出的分数插补已被用于处理回归背景下的缺失值。在这篇文章中，我们开发了分数插补方法，用于估计混杂因素随机缺失的平均治疗效果。我们证明了平均处理效应的分数插补估计是渐近正态的，这允许一致的方差估计。通过模拟研究，比较了分数插补与多重插补的准确度和精度。
The problem of missingness in observational data is ubiquitous. When the confounders are missing at random, multiple imputation is commonly used; however, the method requires congeniality conditions for valid inferences, which may not be satisfied when estimating average causal treatment effects. Alternatively, fractional imputation, proposed by Kim 2011, has been implemented to handling missing values in regression context. In this article, we develop fractional imputation methods for estimating the average treatment effects with confounders missing at random. We show that the fractional imputation estimator of the average treatment effect is asymptotically normal, which permits a consistent variance estimate. Via simulation study, we compare fractional imputation’s accuracy and precision with that of multiple imputation.