Multiple imputation methods for handling incomplete longitudinal and clustered data where the target analysis is a linear mixed effects model.

ABSTRACT: Multiple imputation (MI) is increasingly popular for handling multivariate missing data. Two general approaches are available in standard computer packages: MI based on the posterior distribution of incomplete variables under a multivariate (joint) model, and fully conditional specification (FCS), which imputes missing values using univariate conditional distributions for each incomplete variable given all the others, cycling iteratively through the univariate imputation models. In the context of longitudinal or clustered data, it is not clear whether these approaches result in consistent estimates of regression coefficient and variance component parameters when the analysis model of interest is a linear mixed effects model (LMM) that includes both random intercepts and slopes with either

SUBMITTER: Huque MH 

PROVIDER: S-EPMC7614826 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature