In spatio-temporal disease mapping models, identifiability constraints affect PQL and INLA results
| dc.contributor.author | Goicoa Mangado, Tomás | |
| dc.contributor.author | Adin Urtasun, Aritz | |
| dc.contributor.author | Ugarte Martínez, María Dolores | |
| dc.contributor.author | Hodges, James S. | |
| dc.contributor.department | Institute for Advanced Materials and Mathematics - INAMAT2 | en |
| dc.date.accessioned | 2024-01-18T07:26:30Z | |
| dc.date.available | 2024-01-18T07:26:30Z | |
| dc.date.issued | 2018 | |
| dc.date.updated | 2024-01-18T07:16:03Z | |
| dc.description.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 wellspecified. In this paper the spatial, temporal, and spatiotemporal 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. | en |
| dc.description.sponsorship | This work has been supported by the Spanish Ministry of Economy and Competitiveness (project MTM2014- 51992-R), and by the Health Department of the Navarre Government (Project 113, Res.2186/2014). | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Goicoa, T., Adin, A., Ugarte, M. D., & Hodges, J. S. (2018). In spatio-temporal disease mapping models, identifiability constraints affect PQL and INLA results. Stochastic Environmental Research and Risk Assessment, 32(3), 749-770. https://doi.org/10.1007/s00477-017-1405-0 | en |
| dc.identifier.doi | 10.1007/s00477-017-1405-0 | |
| dc.identifier.issn | 1436-3240 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/47086 | |
| dc.language.iso | eng | en |
| dc.publisher | Springer | en |
| dc.relation.ispartof | Stochastic Environmental Research and Risk Assessment, (2018) 32:749-770 | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//MTM2014-51992-R/ES/ | |
| dc.relation.projectID | info:eu-repo/grantAgreement/Gobierno de Navarra//Project 113/ | |
| dc.relation.publisherversion | https://doi.org/10.1007/s00477-017-1405-0 | |
| dc.rights | © Springer-Verlag Berlin Heidelberg 2017 | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.subject | Breast cancer | en |
| dc.subject | INLA | en |
| dc.subject | Leroux CAR prior | en |
| dc.subject | PQL | en |
| dc.subject | Space-time interactions | en |
| dc.title | In spatio-temporal disease mapping models, identifiability constraints affect PQL and INLA results | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | |
| dspace.entity.type | Publication | |
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