A Bayesian multivariate spatial approach for illness-death survival models

TL;DR

This paper introduces a Bayesian multivariate spatial model for illness-death survival analysis, allowing for assessment of geographical variation in risks and transition probabilities in non-terminal diseases.

Contribution

It develops a novel Bayesian framework using a multivariate Leroux prior for spatial random effects in illness-death models, applied to osteoporotic hip fracture data.

Findings

Identified significant spatial variation in risks and transition probabilities.

Demonstrated the model's ability to assess geographical differences in disease progression.

Provided a computationally efficient Bayesian inference approach using INLA.

Abstract

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).