title: Dealing with missing covariates in epidemiologic studies: a comparison between multiple imputation and a full Bayesian approach.
abstract: Incomplete data are generally a challenge to the analysis of most large studies. The current gold standard to account for missing data is multiple imputation, and more specifically multiple imputation with chained equations (MICE). Numerous studies have been conducted to illustrate the performance of MICE for missing covariate data. The results show that the method works well in various situations. However, less is known about its performance in more complex models, specifically when the outcome is multivariate as in longitudinal studies. In current practice, the multivariate nature of the longitudinal outcome is often neglected in the imputation procedure, or only the baseline outcome is used to impute missing covariates. In this work, we evaluate the performance of MICE using different strategies to include a longitudinal outcome into the imputation models and compare it with a fully Bayesian approach that jointly imputes missing values and estimates the parameters of the longitudinal model. Results from simulation and a real data example show that MICE requires the analyst to correctly specify which components of the longitudinal process need to be included in the imputation models in order to obtain unbiased results. The full Bayesian approach, on the other hand, does not require the analyst to explicitly specify how the longitudinal outcome enters the imputation models. It performed well under different scenarios. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Department of Biostatistics, Erasmus MC, Wytemaweg 80, Rotterdam, 3015CN, The Netherlands.|Department of Epidemiology, Erasmus MC, Wytemaweg 80, Rotterdam, 3015CN, The Netherlands.
authors.forename: Nicole S
authors.initials: NS
authors.lastname: Erler
authors.affiliation: Department of Biostatistics, Erasmus MC, Wytemaweg 80, Rotterdam, 3015CN, The Netherlands.
authors.forename: Dimitris
authors.identifier: 0000-0001-9397-0900
authors.initials: D
authors.lastname: Rizopoulos
authors.affiliation: Department of Biostatistics, Erasmus MC, Wytemaweg 80, Rotterdam, 3015CN, The Netherlands.
authors.forename: Joost van
authors.initials: Jv
authors.lastname: Rosmalen
authors.affiliation: Department of Epidemiology, Erasmus MC, Wytemaweg 80, Rotterdam, 3015CN, The Netherlands.|Department of Pediatrics, Erasmus MC, Wytemaweg 80, Rotterdam, 3015CN, The Netherlands.
authors.forename: Vincent W V
authors.initials: VW
authors.lastname: Jaddoe
authors.affiliation: Department of Epidemiology, Erasmus MC, Wytemaweg 80, Rotterdam, 3015CN, The Netherlands.
authors.forename: Oscar H
authors.initials: OH
authors.lastname: Franco
authors.affiliation: Department of Biostatistics, Erasmus MC, Wytemaweg 80, Rotterdam, 3015CN, The Netherlands.|L-Biostat, KU Leuven, Kapucijnenvoer 35, Leuven, 3000, Belgium.
authors.forename: Emmanuel M E H
authors.initials: EM
authors.lastname: Lesaffre
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016021:Epidemiologic Studies; D008137:Longitudinal Studies; D015233:Models, Statistical
keywords: Bayesian; MICE; epidemiology; missing covariate values; multiple imputation