Predicting cancer incidence in regions without population-based cancer registries using mortality

Retegui Goñi, Garazi; Etxeberria Andueza, Jaione; Riebler, Andrea; Ugarte Martínez, María Dolores

Predicting cancer incidence in regions without population-based cancer registries using mortality

dc.contributor.author	Retegui Goñi, Garazi
dc.contributor.author	Etxeberria Andueza, Jaione
dc.contributor.author	Riebler, Andrea
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.contributor.funder	Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, PJUPNA2018-11	es
dc.date.accessioned	2023-09-11T16:49:12Z
dc.date.available	2023-09-11T16:49:12Z
dc.date.issued	2023
dc.date.updated	2023-09-11T16:28:27Z
dc.description.abstract	Cancer incidence numbers are routinely recorded by national or regional population-based cancer registries (PBCRs). However, in most southern European countries, the local PBCRs cover only a fraction of the country. Therefore, national cancer incidence can be only obtained through estimation methods. In this paper, we predict incidence rates in areas without cancer registry using multivariate spatial models modelling jointly cancer incidence and mortality. To evaluate the proposal, we use cancer incidence and mortality data from all the German states. We also conduct a simulation study by mimicking the real case of Spain considering different scenarios depending on the similarity of spatial patterns between incidence and mortality, the levels of lethality, and varying the amount of incidence data available. The new proposal provides good interval estimates in regions without PBCRs and reduces the relative error in estimating national incidence compared to one of the most widely used methodologies.	en
dc.description.sponsorship	The work was supported by Project PID2020-113125RB-I00/MCIN/AEI/10.13039/ 501100011033, Proyecto Jóvenes Investigadores PJUPNA2018-11 and Ayudas Predoctorales Santander UPNA 2021-2022.	en
dc.format.mimetype	application/pdf	en
dc.identifier.citation	Retegui, G., Etxeberria, J., Riebler, A., Ugarte, M. D. (2023) Predicting cancer incidence in regions without population-based cancer registries using mortality. Journal of the Royal Statistical Society: Series A (Statistics in Society), 1-16. https://doi.org/10.1093/jrsssa/qnad077.	en
dc.identifier.doi	10.1093/jrsssa/qnad077
dc.identifier.issn	0964-1998
dc.identifier.uri	https://academica-e.unavarra.es/handle/2454/46260
dc.language.iso	eng	en
dc.publisher	Oxford University Press	en
dc.relation.ispartof	Journal of the Royal Statistical Society. Series A: Statistics in Society, 2023	en
dc.relation.projectID	info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113125RB-I00/ES/
dc.relation.publisherversion	https://doi.org/10.1093/jrsssa/qnad077
dc.rights	© The Royal Statistical Society 2023. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.	en
dc.rights.accessRights	info:eu-repo/semantics/openAccess
dc.rights.uri	https://creativecommons.org/licenses/by-nc/4.0/
dc.subject	Bayesian inference	en
dc.subject	Cancer incidence	en
dc.subject	Disease mapping	en
dc.subject	Multivariate spatial models	en
dc.subject	Predictions	en
dc.title	Predicting cancer incidence in regions without population-based cancer registries using mortality	en
dc.type	info:eu-repo/semantics/article
dc.type.version	info:eu-repo/semantics/publishedVersion
dspace.entity.type	Publication
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