691 – Methodology: Evaluation Approaches
Propensity Score Analysis with Missing Data: The Comparison of Multiple Imputation Approaches
Eun Sook Kim
University of South Florida
Jeffrey D. Kromrey
University of South Florida
Seang-Hwane Joo
University of South Florida
Yan Wang
University of South Florida
Jessica Montgomery
University of South Florida
Reginald Lee
University of South Florida
Patricia Rodriguez de Gil
University of South Florida
Shetay Ashford
University of South Florida
Rheta Lanehart
University of South Florida
Chunhua Cao
University of South Florida
The appropriate treatment of missing data under different missing data mechanisms is essential for unbiased estimates and correct statistical inferences in propensity score analysis (PSA). This simulation study investigates the efficacy of two missing data techniques (multiple imputation and listwise deletion) in PSA. For multiple imputation, four different approaches are considered in combination of two factors: what to impute (covariates only or PS in concert with covariates) and how to combine multiply imputed data (average treatment effects or average PS). Simulation design factors include sample size (500, 1000), treatment effect magnitude (0, .05, .10, .15), correlation between covariates (0, .50), proportion of missing observations (.20, .40, .60), proportion of missing covariates (.20, .40, .60), the number of covariates (15, 30), and missing data mechanisms (MCAR, MAR, MNAR). The missing data treatments serve as a within group factor. Imputing covariates only, combined with averaging treatment effects estimates across imputations, outperforms other methods under MAR, but none of multiple imputation approaches is apt under MNAR.