longitudinal process as well as for all relevant sets of random effects, parameters and hyperparameters. Joint
models allow to model longitudinal data with non-ignorable missing data mechanisms with the help of survival
tools and to model survival models with internal time-dependent covariates with the help of longitudinal models.
Joint models were introduced during the 1990s in HIV/AIDS and cancer studies and since then have been applied
to a great variety of studies in epidemiological and biomedical areas. They have a great potential in environmental,
agricultural and biological sciences where right now are not so extensively used.
We introduce two Bayesian joint models for two different studies within the medical setting, one for a longitudinal
analysis and one for a survival scenario. The longitudinal study is defined in terms of a linear mixed-effects model
that accounts for heterogeneity between the subjects, serial correlation, and measurement error. Dropout is here
modeled in terms of a survival model with competing risks and left truncation. The survival study uses an ordinal
longitudinal marker modeled in terms of a proportional-odds cumulative logit model and a Cox proportional hazard
model with left truncation for the time to the event of interest.