Imputation of Missing Covariate Data Prior to Propensity Score Analysis: A Tutorial and Evaluation of the Robustness of Practical Approaches

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dc.authoridAYDIN, Burak/0000-0003-4462-1784
dc.authoridLeite, Walter/0000-0001-7655-5668
dc.authorscopusid23393086500
dc.authorscopusid56117513500
dc.authorscopusid57200618030
dc.authorwosidAYDIN, Burak/GRJ-9231-2022
dc.authorwosidAYDIN, Burak/V-9661-2019
dc.contributor.authorLeite, Walter L.
dc.contributor.authorAydin, Burak
dc.contributor.authorCetin-Berber, Dee D.
dc.date.accessioned2023-01-12T20:03:36Z
dc.date.available2023-01-12T20:03:36Z
dc.date.issued2021
dc.departmentN/A/Departmenten_US
dc.description.abstractBackground: 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.en_US
dc.identifier.doi10.1177/0193841X211020245
dc.identifier.endpage69en_US
dc.identifier.issn0193-841X
dc.identifier.issn1552-3926
dc.identifier.issn0193-841Xen_US
dc.identifier.issn1552-3926en_US
dc.identifier.issue1-2en_US
dc.identifier.scopus2-s2.0-85108819862en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage34en_US
dc.identifier.urihttps://doi.org/10.1177/0193841X211020245
dc.identifier.urihttps://hdl.handle.net/11454/77710
dc.identifier.volume45en_US
dc.identifier.wosWOS:000665214400001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSage Publications Incen_US
dc.relation.ispartofEvaluation Reviewen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectpropensity score analysisen_US
dc.subjectmultiple imputationen_US
dc.subjectmultivariate imputation by chained equationsen_US
dc.subjectjoint modelingen_US
dc.subjectquasi-experimental designen_US
dc.subjectMonte Carlo simulationen_US
dc.subjectinverse probability of treatment weightsen_US
dc.subjectMultiple Imputationen_US
dc.subjectChained Equationsen_US
dc.subjectCausal Inferenceen_US
dc.subjectBiasen_US
dc.subjectStatisticsen_US
dc.subjectSelectionen_US
dc.subjectDesignen_US
dc.subjectValuesen_US
dc.titleImputation of Missing Covariate Data Prior to Propensity Score Analysis: A Tutorial and Evaluation of the Robustness of Practical Approachesen_US
dc.typeArticleen_US

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