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- Title
Variance partitioning in spatio-temporal disease mapping models.
- Authors
Franco-Villoria, Maria; Ventrucci, Massimo; Rue, Håvard
- 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.
- Subjects
GAUSSIAN Markov random fields; DISEASE mapping; KRONECKER products
- Publication
Statistical Methods in Medical Research, 2022, Vol 31, Issue 8, p1566
- ISSN
0962-2802
- Publication type
Article
- DOI
10.1177/09622802221099642