Lee Yongseok, Leite Walter L
Bureau of Economic and Business Research (BEBR), University of Florida.
School of Human Development and Organizational Studies in Education, University of Florida.
Psychol Methods. 2024 Jun 13. doi: 10.1037/met0000676.
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).
倾向得分分析（PSA）是减轻观察性研究中选择偏倚的一种重要方法，但协变量中的数据缺失很普遍，在倾向得分估计过程中必须加以处理。通过蒙特卡洛模拟，本研究评估了基于多种随机森林算法的插补方法在处理协变量数据缺失方面的应用：链式方程随机森林多元插补法（Caliber）、临近插补法（PI）和missForest。结果表明，无论样本量和缺失机制如何，PI和missForest在平均治疗效果偏差方面均优于其他方法。利用2010 - 2011年幼儿园班级的幼儿纵向研究数据，展示了这五种方法结合PSA来评估参与中心式照料对儿童阅读能力的影响。（《心理学文摘数据库记录》（c）2024美国心理学会，保留所有权利）
