Propensity Score Weighting with Missing Data on Covariates and Clustered Data Structure

被引:1
|
作者
Liu, Xiao [1 ,2 ]
机构
[1] Univ Texas Austin, Dept Educ Psychol, Austin, TX USA
[2] Univ Texas Austin, Dept Educ Psychol, Austin, TX 78712 USA
关键词
Propensity score weighting; multilevel data; missing data; multiple imputation; causal inference; KINDERGARTEN RETENTION POLICY; MULTIPLE IMPUTATION; CAUSAL INFERENCE; INTRACLASS CORRELATION; CHAINED EQUATIONS; MULTILEVEL MODELS; MONTE-CARLO; STATISTICS; TRIALS; ASSIGNMENT;
D O I
10.1080/00273171.2024.2307529
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
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.
引用
收藏
页码:411 / 433
页数:23
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