A comparison of multiple imputation methods for missing data in longitudinal studies.

ABSTRACT:

Background

Multiple imputation (MI) is now widely used to handle missing data in longitudinal studies. Several MI techniques have been proposed to impute incomplete longitudinal covariates, including standard fully conditional specification (FCS-Standard) and joint multivariate normal imputation (JM-MVN), which treat repeated measurements as distinct variables, and various extensions based on generalized linear mixed models. Although these MI approaches have been implemented in various software packages, there has not been a comprehensive evaluation of the relative performance of these methods in the context of longitudinal data.

Method

Using both empirical data and a simulation study based on data from the six waves of the Longitudinal Study of Australian Children (N = 4661

SUBMITTER: Huque MH 

PROVIDER: S-EPMC6292063 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature