  Multilevel compositional data commonly occur in various fields, particularly
in intensive, longitudinal studies using ecological momentary assessments.
Examples include data repeatedly measured over time that are non-negative and
sum to a constant value, such as sleep-wake movement behaviours in a 24-hour
day. This article presents a novel methodology for analysing multilevel
compositional data using a Bayesian inference approach. This method can be used
to investigate how reallocation of time between sleep-wake movement behaviours
may be associated with other phenomena (e.g., emotions, cognitions) at a daily
level. We explain the theoretical details of the data and the models, and
outline the steps necessary to implement this method. We introduce the R
package multilevelcoda to facilitate the application of this method and
illustrate using a real data example. An extensive parameter recovery
simulation study verified the robust performance of the method. Across all
simulation conditions investigated in the simulation study, the model had
minimal convergence issues (convergence rate > 99%) and achieved excellent
quality of parameter estimates and inference, with an average bias of 0.00
(range -0.09, 0.05) and coverage of 0.95 (range 0.93, 0.97). We conclude the
article with recommendations on the use of the Bayesian compositional
multilevel modelling approach, and hope to promote wider application of this
method to answer robust questions using the increasingly available data from
intensive, longitudinal studies.
