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首页> 外文期刊>Evaluation review >Imputation of Missing Covariate Data Prior to Propensity Score Analysis: A Tutorial and Evaluation of the Robustness of Practical Approaches
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Imputation of Missing Covariate Data Prior to Propensity Score Analysis: A Tutorial and Evaluation of the Robustness of Practical Approaches

机译：在倾向评分分析之前缺少协变量数据的归责：实用方法稳健性的教程和评估

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摘要

Background: Propensity score analysis (PSA) is a popular method to remove selection bias due to covariates in quasi-experimental designs, but it requires handling of missing data on covariates before propensity scores are estimated. Multiple imputation (MI) and single imputation (SI) are approaches to handle missing data in PSA. Objectives: The objectives of this study are to review MI-within, MI-across, and SI approaches to handle missing data on covariates prior to PSA, investigate the robustness of MI-across and SI with a Monte Carlo simulation study, and demonstrate the analysis of missing data and PSA with a step-by-step illustrative example. Research design: The Monte Carlo simulation study compared strategies to impute missing data in continuous and categorical covariates for estimation of propensity scores. Manipulated conditions included sample size, the number of covariates, the size of the treatment effect, missing data mechanism, and percentage of missing data. Imputation strategies included MI-across and SI by joint modeling or multivariate imputation by chained equations (MICE). Results: The results indicated that the MI-across method performed well, and SI also performed adequately with smaller percentages of missing data. The illustrative example demonstrated MI and SI, propensity score estimation, calculation of propensity score weights, covariate balance evaluation, estimation of the average treatment effect on the treated, and sensitivity analysis using data from the National Longitudinal Survey of Youth.
机译：背景：倾向评分分析（PSA）是一种流行的方法，可以避免由于准实验设计中的协变量引起的选择偏差，但需要在估计倾向分数之前处理缺失数据的协调因子。多个归纳（MI）和单个估算（SI）是处理PSA中缺失数据的方法。目的：本研究的目标是审查MI-IN，MI-OD，以及SI方法来处理PSA之前的协变量的缺失数据，调查MI-THING和SI的鲁棒性和SI与蒙特卡罗模拟研究，并证明了逐步说明性示例分析缺失数据和PSA。研究设计：蒙特卡罗模拟研究比较策略，以估计倾向分数的连续和分类协变量中缺失数据。被操纵条件包括样本大小，协变量的数量，治疗效果的大小，缺少数据机制，以及缺失数据的百分比。由于链接方程（小鼠），通过联合建模或多变量归模包括MI-跨越和SI。结果：结果表明，跨越方法表现良好，SI也充分地进行了较小的缺失数据百分比。该说明性实施例证明了MI和Si，倾向评分估计，倾向评分重量的计算，协变量平衡评估，估计对治疗的综合治疗效果的平均治疗效果，以及使用国民纵向调查中的数据的敏感性分析。

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