Evaluation of approaches for accommodating interactions and non-linear terms in multiple imputation of incomplete three-level data.

ABSTRACT: Three-level data structures arising from repeated measures on individuals clustered within larger units are common in health research studies. Missing data are prominent in such studies and are often handled via multiple imputation (MI). Although several MI approaches can be used to account for the three-level structure, including adaptations to single- and two-level approaches, when the substantive analysis model includes interactions or quadratic effects, these too need to be accommodated in the imputation model. In such analyses, substantive model compatible (SMC) MI has shown great promise in the context of single-level data. Although there have been recent developments in multilevel SMC MI, to date only one approach that explicitly handles incomplete three-level data is available. Alt

SUBMITTER: Wijesuriya R 

PROVIDER: S-EPMC10174217 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature