TY - JOUR
T1 - Variance partitioning in spatio-temporal disease mapping models
AU - Franco-Villoria, Maria
AU - Ventrucci, Massimo
AU - Rue, Haavard
N1 - KAUST Repository Item: Exported on 2022-04-12
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/672082
UR - https://arxiv.org/pdf/2109.13374.pdf
M3 - Article
JO - Accepted for publication in Statistical Methods in Medical Research
JF - Accepted for publication in Statistical Methods in Medical Research
ER -
