Bayesian imputation of time-varying covariates in linear mixed models.

ABSTRACT: Studies involving large observational datasets commonly face the challenge of dealing with multiple missing values. The most popular approach to overcome this challenge, multiple imputation using chained equations, however, has been shown to be sub-optimal in complex settings, specifically in settings with longitudinal outcomes, which cannot be easily and adequately included in the imputation models. Bayesian methods avoid this difficulty by specification of a joint distribution and thus offer an alternative. A popular choice for that joint distribution is the multivariate normal distribution. In more complicated settings, as in our two motivating examples that involve time-varying covariates, additional issues require consideration: the endo- or exogeneity of the covariate and its functio

SUBMITTER: Erler NS 

PROVIDER: S-EPMC6344996 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature