Yucel Karakaya Sevinc Puren, Unal Ilker
Department of Biostatistics, School of Medicine, Cukurova University, Turkey.
Stat Methods Med Res. 2025 Jul;34(7):1293-1302. doi: 10.1177/09622802251338365. Epub 2025 Jun 19.
One of the primary challenges encountered in propensity score (PS) weighting is the presence of observations with missing covariates. In such cases, several potential solutions based on multiple imputation have been proposed. The most prevalent of these is the MI method, which combines treatment effect estimates derived from imputed datasets. A limited number of PS studies have incorporated the MI method with the missing indicator method; however, these studies only incorporated the missing indicator into the PS model. The aim of this simulation study is to propose two novel methods that incorporate the missing indicator approach with the MI. This incorporation either entails including the missing indicator into the outcome model (MIMI) or, alternatively, into both the outcome and PS model (MIMI). The construction of the simulation scenarios was predicated on three elements: the mechanism of missing data, the type of treatment effect, and the presence of unmeasured confounding. In the presence of unmeasured confounding, the MIMI method was the most effective method under the MAR mechanism. In the context of the MNAR mechanism, the method that exhibited the lowest bias was MIMI for homogeneous treatment effect and MIMI for heterogeneous treatment effect. The MI method exhibited the highest levels of bias and variation. In view of the difficulties involved in identifying the mechanism of missing data, the variability in treatment effects across subgroups and the potential for unmeasured confounding variables in practice, researchers are encouraged to utilize the MIMI method.
倾向得分（PS）加权中遇到的主要挑战之一是存在协变量缺失的观测值。在这种情况下，已经提出了几种基于多重填补的潜在解决方案。其中最普遍的是MI方法，它结合了从填补数据集得出的治疗效果估计值。有限数量的PS研究将MI方法与缺失指标方法相结合；然而，这些研究只是将缺失指标纳入了PS模型。本模拟研究的目的是提出两种将缺失指标方法与MI相结合的新方法。这种结合要么是将缺失指标纳入结果模型（MIMI），要么是将其纳入结果模型和PS模型（MIMI）。模拟场景的构建基于三个要素：缺失数据的机制、治疗效果的类型以及未测量混杂因素的存在。在存在未测量混杂因素的情况下，MIMI方法是MAR机制下最有效的方法。在MNAR机制的背景下，对于同质治疗效果，表现出最低偏差的方法是MIMI；对于异质治疗效果，也是MIMI。MI方法表现出最高水平的偏差和变异性。鉴于在实际中识别缺失数据机制、各亚组治疗效果的变异性以及未测量混杂变量的可能性所涉及的困难，鼓励研究人员使用MIMI方法。
