  The use of longitudinal finite mixture models such as group-based trajectory
modeling has seen a sharp increase during the last decades in the medical
literature. However, these methods have been criticized especially because of
the data-driven modelling process which involves statistical decision-making.
In this paper, we propose an approach that uses bootstrap to sample
observations with replacement from the original data to validate the number of
groups identified and to quantify the uncertainty in the number of groups. The
method allows investigating the statistical validity and the uncertainty of the
groups identified in the original data by checking if the same solution is also
found across the bootstrap samples. In a simulation study, we examined whether
the bootstrap-estimated variability in the number of groups reflected the
replication-wise variability. We also compared the replication-wise variability
to the Bayesian posterior probability. We evaluated the ability of three
commonly used adequacy criteria (average posterior probability, odds of correct
classification and relative entropy) to identify uncertainty in the number of
groups. Finally, we illustrated the proposed approach using data from the
Quebec Integrated Chronic Disease Surveillance System to identify longitudinal
medication patterns between 2015 and 2018 in older adults with diabetes.
