Comparison of methods for handling covariate missingness in propensity score estimation with a binary exposure.

ABSTRACT:

Background

Causal effect estimation with observational data is subject to bias due to confounding, which is often controlled for using propensity scores. One unresolved issue in propensity score estimation is how to handle missing values in covariates.

Method

Several approaches have been proposed for handling covariate missingness, including multiple imputation (MI), multiple imputation with missingness pattern (MIMP), and treatment mean imputation. However, there are other potentially useful approaches that have not been evaluated, including single imputation (SI) + prediction error (PE), SI + PE + parameter uncertainty (PU), and Generalized Boosted Modeling (GBM), which is a nonparametric approach for estimating propensity scores in which missing values are automatically h

SUBMITTER: Coffman DL 

PROVIDER: S-EPMC7318364 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature