  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 approaches have been proposed to impute
incomplete longitudinal data, such as treating repeated measurements of the
same variable as distinct variables or using generalized linear mixed
imputation models. However, the uptake of these methods has been limited, as
they require additional data manipulation and use of advanced imputation
procedures. In this tutorial, we review the available MI approaches that can be
used for handling incomplete longitudinal data, including where individuals are
clustered within higher-level clusters. We illustrate implementation with
replicable R and Stata code using a case study from the Childhood to
Adolescence Transition Study.
