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- Title
In spatio-temporal disease mapping models, identifiability constraints affect PQL and INLA results.
- Authors
Goicoa, T.; Adin, A.; Ugarte, M. D.; Hodges, J. S.
- Abstract
Disease mapping studies the distribution of relative risks or rates in space and time, and typically relies on generalized linear mixed models (GLMMs) including fixed effects and spatial, temporal, and spatio-temporal random effects. These GLMMs are typically not identifiable and constraints are required to achieve sensible results. However, automatic specification of constraints can sometimes lead to misleading results. In particular, the penalized quasi-likelihood fitting technique automatically centers the random effects even when this is not necessary. In the Bayesian approach, the recently-introduced integrated nested Laplace approximations computing technique can also produce wrong results if constraints are not well-specified. In this paper the spatial, temporal, and spatio-temporal interaction random effects are reparameterized using the spectral decompositions of their precision matrices to establish the appropriate identifiability constraints. Breast cancer mortality data from Spain is used to illustrate the ideas.
- Subjects
SPAIN; DISEASE mapping; BREAST cancer statistics; CANCER-related mortality; LINEAR statistical models; FIXED effects model; LAPLACE distribution; MORTALITY
- Publication
Stochastic Environmental Research & Risk Assessment, 2018, Vol 32, Issue 3, p749
- ISSN
1436-3240
- Publication type
Article
- DOI
10.1007/s00477-017-1405-0