: 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 model. The variance partitioning 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 an intuitive way. We illustrate the advantages of the variance partitioning model using two case studies.

Variance partitioning in spatio-temporal disease mapping models / Franco-Villoria, Maria; Ventrucci, Massimo; Rue, Håvard. - In: STATISTICAL METHODS IN MEDICAL RESEARCH. - ISSN 0962-2802. - 31:(2022), pp. 1566-1578. [10.1177/09622802221099642]

Variance partitioning in spatio-temporal disease mapping models

Franco-Villoria, Maria
;
2022

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 model. The variance partitioning 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 an intuitive way. We illustrate the advantages of the variance partitioning model using two case studies.
31
1566
1578
Variance partitioning in spatio-temporal disease mapping models / Franco-Villoria, Maria; Ventrucci, Massimo; Rue, Håvard. - In: STATISTICAL METHODS IN MEDICAL RESEARCH. - ISSN 0962-2802. - 31:(2022), pp. 1566-1578. [10.1177/09622802221099642]
Franco-Villoria, Maria; Ventrucci, Massimo; Rue, Håvard
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1287464
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