Evaluation of approaches for multiple imputation of three-level data.

ABSTRACT:

Background

Three-level data arising from repeated measures on individuals who are clustered within larger units are common in health research studies. Missing data are prominent in such longitudinal studies and multiple imputation (MI) is a popular approach for handling missing data. Extensions of joint modelling and fully conditional specification MI approaches based on multilevel models have been developed for imputing three-level data. Alternatively, it is possible to extend single- and two-level MI methods to impute three-level data using dummy indicators and/or by analysing repeated measures in wide format. However, most implementations, evaluations and applications of these approaches focus on the context of incomplete two-level data. It is currently unclear which approach is

SUBMITTER: Wijesuriya R 

PROVIDER: S-EPMC7422505 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature