Dealing with risk discontinuities to estimate cancer mortality risks when the number of small areas is large
| dc.contributor.author | Santafé Rodrigo, Guzmán | |
| dc.contributor.author | Adin Urtasun, Aritz | |
| dc.contributor.author | Lee, Duncan | |
| dc.contributor.author | Ugarte Martínez, María Dolores | |
| dc.contributor.department | Estadística, Informática y Matemáticas | es_ES |
| dc.contributor.department | Estatistika, Informatika eta Matematika | eu |
| dc.contributor.department | Institute for Advanced Materials and Mathematics - INAMAT2 | en |
| dc.date.accessioned | 2025-01-23T08:07:34Z | |
| dc.date.available | 2025-01-23T08:07:34Z | |
| dc.date.issued | 2021-02-17 | |
| dc.date.updated | 2025-01-23T08:03:47Z | |
| dc.description.abstract | Many statistical models have been developed during the last years to smooth risks in disease mapping. However, most of these modeling approaches do not take possible local discontinuities into consideration or if they do, they are computationally prohibitive or simply do not work when the number of small areas is large. In this paper, we propose a two-step method to deal with discontinuities and to smooth noisy risks in small areas. In a first stage, a novel density-based clustering algorithm is used. In contrast to previous proposals, this algorithm is able to automatically detect the number of spatial clusters, thus providing a single cluster structure. In the second stage, a Bayesian hierarchical spatial model that takes the cluster configuration into account is fitted, which accounts for the discontinuities in disease risk. To evaluate the performance of this new procedure in comparison to previous proposals, a simulation study has been conducted. Results show competitive risk estimates at a much better computational cost. The new methodology is used to analyze stomach cancer mortality data in Spanish municipalities. | en |
| dc.description.sponsorship | The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Adin, A., Santé, G., and Ugarte, M.D. research has been supported by project MTM2017-82553-R (AEI/FEDER, UE). Lee, D. research has been supported by the UK Medical Research Council (Grant number MR/L022184/1). | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Santafé, G., Adin, A., Lee, D., Ugarte, M.D. (2021) Dealing with risk discontinuities to estimate cancer mortality risks when the number of small areas is large. Statistical methods in medical research an international review journal, 30(1), 6-21. https://doi.org/10.1177/0962280220946502 | |
| dc.identifier.doi | 10.1177/0962280220946502 | |
| dc.identifier.issn | 0962-2802 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/53058 | |
| dc.language.iso | eng | |
| dc.publisher | SAGE | |
| dc.relation.ispartof | Statistical methods in medical research an international review journal, 30(1), 6-21 | |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2017-82553-R/ES/ | |
| dc.relation.publisherversion | https://doi.org/10.1177/0962280220946502 | |
| dc.rights | © 2020 by SAGE Publications. The Author(s). | |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.subject | Clustering | en |
| dc.subject | Disease mapping | en |
| dc.subject | INLA | en |
| dc.subject | Small areas | en |
| dc.subject | Smoothing | en |
| dc.subject | Spanish municipalities | en |
| dc.subject | Stomach cancer | en |
| dc.title | Dealing with risk discontinuities to estimate cancer mortality risks when the number of small areas is large | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | |
| dspace.entity.type | Publication | |
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