Integrating latent classes in the Bayesian shared parameter joint model of longitudinal and survival outcomes.

ABSTRACT: Cystic fibrosis is a chronic lung disease requiring frequent lung-function monitoring to track acute respiratory events (pulmonary exacerbations). The association between lung-function trajectory and time-to-first exacerbation can be characterized using joint longitudinal-survival modeling. Joint models specified through the shared parameter framework quantify the strength of association between such outcomes but do not incorporate latent sub-populations reflective of heterogeneous disease progression. Conversely, latent class joint models explicitly postulate the existence of sub-populations but do not directly quantify the strength of association. Furthermore, choosing the optimal number of classes using established metrics like deviance information criterion is computationally intensive

SUBMITTER: Andrinopoulou ER 

PROVIDER: S-EPMC7545534 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature