Variance partitioning in spatio-temporal disease mapping models

TL;DR

This paper introduces a variance partitioning (VP) model for Bayesian spatio-temporal disease mapping that improves interpretability by balancing main and interaction effects through a mixing parameter and a penalized complexity prior.

Contribution

The paper presents a reparametrized spatio-temporal interaction model with a mixing parameter for better interpretability and a PC prior for prior elicitation.

Findings

Enhanced interpretability of variance components

Effective balancing of main and interaction effects

Successful application in two case studies

Abstract

Bayesian disease mapping, yet if undeniably useful to describe variation in risk over time and space, comes with the hurdle of prior elicitation on hard-to-interpret random effect precision parameters. We introduce a reparametrized version of the popular spatio-temporal interaction models, based on Kronecker product intrinsic Gaussian Markov Random Fields, that we name the variance partitioning (VP) model. The VP model includes a mixing parameter that balances the contribution of the main and interaction effects to the total (generalized) variance and enhances interpretability. The use of a penalized complexity prior on the mixing parameter aids in coding prior information in a intuitive way. We illustrate the advantages of the VP model using two case studies.