Publication: Estimating Country-Specific Incidence Rates of Rare Cancers: Comparative Performance Analysis of Modeling Approaches Using European Cancer Registry Data
| dc.contributor.author | Botta, L | |
| dc.contributor.author | Martínez, JM | |
| dc.contributor.author | Trama, A | |
| dc.contributor.author | Gatta, G | |
| dc.contributor.author | Borràs, J | |
| dc.contributor.author | Capocaccia, R | |
| dc.contributor.author | Clèries, R | |
| dc.contributor.author | Salmerón Martínez, Diego | |
| dc.contributor.department | Ciencias Sociosanitarias | |
| dc.date.accessioned | 2024-01-12T11:37:28Z | |
| dc.date.available | 2024-01-12T11:37:28Z | |
| dc.date.issued | 2022-10-29 | |
| dc.description | © 2021. The authors. This document is made available under the CC-BY-NC 4.0 license http://creativecommons.org/licenses/by-nc /4.0/ This document is the submitted version of a published work that appeared in final form in American Journal of Epidemiology. | es |
| dc.description.abstract | Estimating incidence of rare cancers is challenging for exceptionally rare entities and in small populations. In a previous study, investigators in the Information Network on Rare Cancers (RARECARENet) provided Bayesian estimates of expected numbers of rare cancers and 95% credible intervals for 27 European countries, using data collected by population-based cancer registries. In that study, slightly different results were found by implementing a Poisson model in integrated nested Laplace approximation/WinBUGS platforms. In this study, we assessed the performance of a Poisson modeling approach for estimating rare cancer incidence rates, oscillating around an overall European average and using small-count data in different scenarios/computational platforms. First, we compared the performance of frequentist, empirical Bayes, and Bayesian approaches for providing 95% confidence/credible intervals for the expected rates in each country. Second, we carried out an empirical study using 190 rare cancers to assess different lower/upper bounds of a uniform prior distribution for the standard deviation of the random effects. For obtaining a reliable measure of variability for country-specific incidence rates, our results suggest the suitability of using 1 as the lower bound for that prior distribution and selecting the random-effects model through an averaged indicator derived from 2 Bayesian model selection criteria: the deviance information criterion and the Watanabe-Akaike information criterion. | es |
| dc.format | application/pdf | es |
| dc.format.extent | 12 | es |
| dc.identifier.citation | American Journal of Epidemiology, Volume 191, Issue 3, March 2022, Pages 487–498 | |
| dc.identifier.doi | https://doi.org/10.1093/aje/kwab262 | |
| dc.identifier.issn | Electronic: 1476-6256 | |
| dc.identifier.issn | Print: 0002-9262 | |
| dc.identifier.uri | http://hdl.handle.net/10201/137234 | |
| dc.language | eng | es |
| dc.publisher | Oxford University Press Inc | es |
| dc.relation | European Commission through the Consumers, Health, Agriculture and Food Executive Agency (grant 2000111201). We acknowledge the support of the Agència d’Avaluació d’Universitats i Recerca (grant 2017SGR00735) and the Centres de Recerca de Catalunya (CERCA) Program of the Generalitat de Catalunya. We also acknowledge support from the Fundación SéNeCa—Agencia de Ciencia y Tecnología de la Región de Murcia Program for Excellence in Scientific Research (project 20862/PI/18). The data underlying this article were provided by RARECARENet by permission. Data will be shared upon request to the corresponding author with the permission of RARECARENet (http://rarecarenet.istitutotumori.mi.it/ rarecarenet/). | es |
| dc.relation.publisherversion | https://academic.oup.com/aje/article/191/3/487/6413874 | es |
| dc.rights | info:eu-repo/semantics/openAccess | es |
| dc.rights | Atribución-NoComercial 4.0 Internacional | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | * |
| dc.title | Estimating Country-Specific Incidence Rates of Rare Cancers: Comparative Performance Analysis of Modeling Approaches Using European Cancer Registry Data | es |
| dc.type | info:eu-repo/semantics/article | es |
| dspace.entity.type | Publication | es |
| relation.isAuthorOfPublication | eb66f6d0-47c9-4f9b-bc79-04137a6b2d59 | |
| relation.isAuthorOfPublication.latestForDiscovery | eb66f6d0-47c9-4f9b-bc79-04137a6b2d59 |
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