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

A Bayesian multivariate spatial approach for illness-death survival models