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

• Published in: BMC medical research methodology Vol. 20; no. 1; pp. 168 - 14
• Main Authors: Coffman, Donna L., Zhou, Jiangxiu, Cai, Xizhen
• Format: Journal Article
• Language: English
• Published: London BioMed Central 26.06.2020; BioMed Central Ltd; BMC
• Subjects: Analysis; Bias; Causal inference; Data analysis; Datasets; Estimates; Generalized boosted models; Health Sciences; Medical research; Medicine; Medicine & Public Health; Methods; Missing data; Missing observations (Statistics); Observational studies; Propensity scores; Research Article; Research methodology; Simulation; Statistical Theory and Methods; statistics and modelling; Statistics for Life Sciences; Theory of Medicine/Bioethics; Variables; United States; Causal inference; Missing data; Propensity scores; Generalized boosted models
• ISSN: 1471-2288; 1471-2288
• DOI: 10.1186/s12874-020-01053-4

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 handled in the estimation using a surrogate split method. To evaluate the performance of these approaches, a simulation study was conducted. Results Results suggested that SI + PE, SI + PE + PU, MI, and MIMP perform almost equally well and better than treatment mean imputation and GBM in terms of bias; however, MI and MIMP account for the additional uncertainty of imputing the missingness. Conclusions Applying GBM to the incomplete data and relying on the surrogate split approach resulted in substantial bias. Imputation prior to implementing GBM is recommended.