Multiple imputation approaches for handling incomplete three-level data with time-varying cluster-memberships.

ABSTRACT: Three-level data arising from repeated measures on individuals clustered within higher-level units are common in medical research. A complexity arises when individuals change clusters over time, resulting in a cross-classified data structure. Missing values in these studies are commonly handled via multiple imputation (MI). If the three-level, cross-classified structure is modeled in the analysis, it also needs to be accommodated in the imputation model to ensure valid results. While incomplete three-level data can be handled using various approaches within MI, the performance of these in the cross-classified data setting remains unclear. We conducted simulations under a range of scenarios to compare these approaches in the context of an acute-effects cross-classified random effects substa

SUBMITTER: Wijesuriya R 

PROVIDER: S-EPMC9540355 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature