具有协变量缺失数据和聚类数据结构的倾向得分加权。

IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Multivariate Behavioral Research Pub Date : 2024-05-01 Epub Date: 2024-02-20 DOI:10.1080/00273171.2024.2307529
Xiao Liu
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引用次数: 0

摘要

倾向得分(PS)分析在行为科学领域越来越受欢迎。有两个问题常常会增加倾向评分分析的复杂性,包括观测协变量数据的缺失和聚类数据结构。在以往的研究中,研究人员研究了在单独考虑其中一个问题的情况下进行 PS 分析的方法。在实践中,这两个问题往往同时存在;但在同时存在这两个问题的情况下,进行 PS 分析的方法的性能以前还没有进行过评估。在本研究中,我们考虑了数据聚类和观测协变量有缺失值时的 PS 加权分析。我们进行了一项模拟研究,以评估不同的缺失数据处理方法(完整病例、单水平估算或多水平估算)与不同的多水平 PS 加权方法(固定或随机效应 PS 模型、反倾向加权或聚类加权、加权单水平或多水平结果模型)相结合的性能。结果表明,通过在缺失数据处理阶段(如多水平估算)和 PS 分析阶段(如固定效应 PS 模型、聚类加权和加权多水平结果模型)更好地考虑聚类,可以减少平均治疗效果估计的偏差。现提供一个真实数据示例以作说明。
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Propensity Score Weighting with Missing Data on Covariates and Clustered Data Structure.

Propensity score (PS) analyses are increasingly popular in behavioral sciences. Two issues often add complexities to PS analyses, including missing data in observed covariates and clustered data structure. In previous research, methods for conducting PS analyses with considering either issue alone were examined. In practice, the two issues often co-occur; but the performance of methods for PS analyses in the presence of both issues has not been evaluated previously. In this study, we consider PS weighting analysis when data are clustered and observed covariates have missing values. A simulation study is conducted to evaluate the performance of different missing data handling methods (complete-case, single-level imputation, or multilevel imputation) combined with different multilevel PS weighting methods (fixed- or random-effects PS models, inverse-propensity-weighting or the clustered weighting, weighted single-level or multilevel outcome models). The results suggest that the bias in average treatment effect estimation can be reduced, by better accounting for clustering in both the missing data handling stage (such as with the multilevel imputation) and the PS analysis stage (such as with the fixed-effects PS model, clustered weighting, and weighted multilevel outcome model). A real-data example is provided for illustration.

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来源期刊
Multivariate Behavioral Research
Multivariate Behavioral Research 数学-数学跨学科应用
CiteScore
7.60
自引率
2.60%
发文量
49
审稿时长
>12 weeks
期刊介绍: Multivariate Behavioral Research (MBR) publishes a variety of substantive, methodological, and theoretical articles in all areas of the social and behavioral sciences. Most MBR articles fall into one of two categories. Substantive articles report on applications of sophisticated multivariate research methods to study topics of substantive interest in personality, health, intelligence, industrial/organizational, and other behavioral science areas. Methodological articles present and/or evaluate new developments in multivariate methods, or address methodological issues in current research. We also encourage submission of integrative articles related to pedagogy involving multivariate research methods, and to historical treatments of interest and relevance to multivariate research methods.
期刊最新文献
Why You Should Not Estimate Mediated Effects Using the Difference-in-Coefficients Method When the Outcome is Binary. A Causal View on Bias in Missing Data Imputation: The Impact of Evil Auxiliary Variables on Norming of Test Scores. Make Some Noise: Generating Data from Imperfect Factor Models. Exploring Estimation Procedures for Reducing Dimensionality in Psychological Network Modeling. A Review of Some of the History of Factorial Invariance and Differential Item Functioning.
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