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.

中文翻译：

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在倾向评分分析之前对缺失协变量数据进行插补：实用方法稳健性的教程和评估

背景：

倾向评分分析 (PSA) 是消除由于准实验设计中的协变量导致的选择偏差的一种流行方法，但它需要在估计倾向评分之前处理协变量的缺失数据。多重插补 (MI) 和单一插补 (SI) 是处理 PSA 中缺失数据的方法。

目标：

本研究的目的是审查在 PSA 之前处理协变量缺失数据的 MI-within、MI-cross 和 SI 方法，通过 Monte Carlo 模拟研究调查 MI-across 和 SI 的稳健性，并证明对缺失数据和 PSA 与分步说明性示例。

研究设计：

Monte Carlo 模拟研究比较了在连续和分类协变量中估算缺失数据以估计倾向得分的策略。操纵条件包括样本大小、协变量的数量、治疗效果的大小、缺失数据机制和缺失数据的百分比。插补策略包括通过联合建模的 MI-across 和 SI 或通过链式方程 (MICE) 的多变量插补。

结果：

结果表明 MI-across 方法表现良好，SI 也表现良好，缺失数据的百分比较小。示例说明了 MI 和 SI、倾向得分估计、倾向得分权重的计算、协变量平衡评估、对被治疗者的平均治疗效果的估计以及使用来自全国青年纵向调查的数据的敏感性分析。