  Illness-death models are a class of stochastic models inside the multi-state
framework. In those models, individuals are allowed to move over time between
different states related to illness and death. They are of special interest
when working with non-terminal diseases, as they not only consider the
competing risk of death but also allow to study progression from illness to
death. The intensity of each transition can be modelled including both fixed
and random effects of covariates. In particular, spatially structured random
effects or their multivariate versions can be used to assess spatial
differences between regions and among transitions. We propose a Bayesian
methodological framework based on an illness-death model with a multivariate
Leroux prior for the random effects. We apply this model to a cohort study
regarding progression after osteoporotic hip fracture in elderly patients. From
this spatial illness-death model we assess the geographical variation in risks,
cumulative incidences, and transition probabilities related to recurrent hip
fracture and death. Bayesian inference is done via the integrated nested
Laplace approximation (INLA).
