Multiple Imputation for Longitudinal Data: A Tutorial.

ABSTRACT: Longitudinal studies are frequently used in medical research and involve collecting repeated measures on individuals over time. Observations from the same individual are invariably correlated and thus an analytic approach that accounts for this clustering by individual is required. While almost all research suffers from missing data, this can be particularly problematic in longitudinal studies as participation often becomes harder to maintain over time. Multiple imputation (MI) is widely used to handle missing data in such studies. When using MI, it is important that the imputation model is compatible with the proposed analysis model. In a longitudinal analysis, this implies that the clustering considered in the analysis model should be reflected in the imputation process. Several MI appro

SUBMITTER: Wijesuriya R 

PROVIDER: S-EPMC11755704 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature