Propensity score analysis (PSA) is a prominent method to alleviate selection bias in observational studies, but missing data in covariates is prevalent and must be dealt with during propensity score estimation. Through Monte Carlo simulations, this study evaluates the use of imputation methods based on multiple random forests algorithms to handle missing data in covariates: multivariate imputation by chained equations-random forest (Caliber), proximity imputation (PI), and missForest. The results indicated that PI and missForest outperformed other methods with respect to bias of average treatment effect regardless of sample size and missing mechanisms. A demonstration of these five methods with PSA to evaluate the effect of participation in center-based care on children's reading ability is provided using data from the Early Childhood Longitudinal Study, Kindergarten Class of 2010-2011. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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倾向评分分析中协变量基于随机森林的缺失插补方法的比较。


倾向评分分析 (PSA) 是减轻观察性研究中选择偏差的一种重要方法，但协变量数据缺失的现象很普遍，必须在倾向评分估计过程中予以处理。通过蒙特卡罗模拟，本研究评估了使用基于多种随机森林算法的插补方法来处理协变量中的缺失数据：通过链式方程进行多元插补 - 随机森林 (Calibre)、邻近插补 (PI) 和 missForest。结果表明，无论样本大小和缺失机制如何，PI 和 missForest 在平均治疗效果偏差方面均优于其他方法。使用 2010-2011 年幼儿园班级早期儿童纵向研究的数据，展示了这五种 PSA 方法，用于评估参与中心护理对儿童阅读能力的影响。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。