Propensity score analysis with partially observed covariates: How should multiple imputation be used?

ABSTRACT: Inverse probability of treatment weighting is a popular propensity score-based approach to estimate marginal treatment effects in observational studies at risk of confounding bias. A major issue when estimating the propensity score is the presence of partially observed covariates. Multiple imputation is a natural approach to handle missing data on covariates: covariates are imputed and a propensity score analysis is performed in each imputed dataset to estimate the treatment effect. The treatment effect estimates from each imputed dataset are then combined to obtain an overall estimate. We call this method MIte. However, an alternative approach has been proposed, in which the propensity scores are combined across the imputed datasets (MIps). Therefore, there are remaining uncertainties abo

SUBMITTER: Leyrat C 

PROVIDER: S-EPMC6313366 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature