Using mortality to predict incidence for rare and lethal cancers in very small areas
| dc.contributor.author | Etxeberria Andueza, Jaione | |
| dc.contributor.author | Goicoa Mangado, Tomás | |
| 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 | 2023-02-14T13:19:28Z | |
| dc.date.available | 2023-02-14T13:19:28Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2023-02-14T13:06:49Z | |
| dc.description.abstract | Incidence and mortality figures are needed to get a comprehensive overview of cancer burden. In many countries, cancer mortality figures are routinely recorded by statistical offices, whereas incidence depends on regional cancer registries. However, due to the complexity of updating cancer registries, incidence numbers become available 3 or 4 years later than mortality figures. It is, therefore, necessary to develop reliable procedures to predict cancer incidence at least until the period when mortality data are available. Most of the methods proposed in the literature are designed to predict total cancer (except nonmelanoma skin cancer) or major cancer sites. However, less frequent lethal cancers, such as brain cancer, are generally excluded from predictions because the scarce number of cases makes it difficult to use univariate models. Our proposal comes to fill this gap and consists of modeling jointly incidence and mortality data using spatio-temporal models with spatial and age shared components. This approach allows for predicting lethal cancers improving the performance of individual models when data are scarce by taking advantage of the high correlation between incidence and mortality. A fully Bayesian approach based on integrated nested Laplace approximations is considered for model fitting and inference. A validation process is also conducted to assess the performance of alternative models. We use the new proposals to predict brain cancer incidence rates by gender and age groups in the health units of Navarre and Basque Country (Spain) during the period 2005-2008. | en |
| dc.description.sponsorship | Spanish Reseach Agency, Grant/Award Number: PID2020-113125RBI-00/MCIN/AEI/10.13039/50110001103; Universidad Publica de Navarra, Grant/Award Number: PJUPNA2018-11 | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Etxeberria, J., Goicoa, T., & Ugarte, M. D. (2022). Using mortality to predict incidence for rare and lethal cancers in very small areas. Biometrical Journal, bimj.202200017. https://doi.org/10.1002/bimj.202200017 | en |
| dc.identifier.doi | 10.1002/bimj.202200017 | |
| dc.identifier.issn | 0323-3847 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/44686 | |
| dc.language.iso | eng | en |
| dc.publisher | VCH Publishers | en |
| dc.relation.ispartof | Biometrical Journal 2022;1–17 | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113125RBI00/ | |
| dc.relation.publisherversion | https://doi.org/10.1002/bimj.202200017 | |
| dc.rights | © 2022 The Authors. Creative Commons Attribution 4.0 International (CC BY 4.0) | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Brain cancer incidence | en |
| dc.subject | Disease mapping | en |
| dc.subject | INLA | en |
| dc.subject | Predictions | en |
| dc.subject | Shared component models | en |
| dc.title | Using mortality to predict incidence for rare and lethal cancers in very small areas | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/publishedVersion | |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | 7770971e-9acf-45ee-bb1a-93bf8b5f1c73 | |
| relation.isAuthorOfPublication | 77ba75f3-a30d-4f01-8cc5-9de6d3a10d8d | |
| relation.isAuthorOfPublication | e87ff19e-9d36-4286-989b-cafd391dff9d | |
| relation.isAuthorOfPublication.latestForDiscovery | 7770971e-9acf-45ee-bb1a-93bf8b5f1c73 |