Variance partitioning in spatio-temporal disease mapping models

Maria Franco-Villoria, Massimo Ventrucci, Haavard Rue

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish (US)
JournalAccepted for publication in Statistical Methods in Medical Research
StatePublished - 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-04-12


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