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Cystic fibrosis is a chronic lung disease which requires frequent patientmonitoring to maintain lung function over time and minimize onset of acuterespiratory events known as pulmonary exacerbations. From the clinical point ofview it is important to characterize the association between key biomarkerssuch as $FEV_1$ and time-to first exacerbation. Progression of the disease isheterogeneous, yielding different sub-groups in the population exhibitingdistinct longitudinal profiles. It is desirable to categorize these unobservedsub-groups (latent classes) according to their distinctive trajectories.Accounting for these latent classes, in other words heterogeneity, will lead toimproved estimates of association arising from the joint longitudinal-survivalmodel.  The joint model of longitudinal and survival data constitutes a popularframework to analyze such data arising from heterogeneous cohorts. Inparticular, two paradigms within this framework are the shared parameter jointmodels and the joint latent class models. The former paradigm allows one toquantify the strength of the association between the longitudinal and survivaloutcomes but does not allow for latent sub-populations. The latter paradigmexplicitly postulates the existence of sub-populations but does not directlyquantify the strength of the association.  We propose to integrate latent classes in the shared parameter joint model ina fully Bayesian approach, which allows us to investigate the associationbetween $FEV_1$ and time-to first exacerbation within each latent class. We,furthermore, focus on the selection of the optimal number of latent classes.

Integrating Latent Classes in the Bayesian Shared Parameter Joint Model of Longitudinal and Survival Outcomes