Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/187901
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dc.contributor.authorSalmerón, Diego-
dc.contributor.authorBotta, Laura-
dc.contributor.authorMartínez, José Miguel-
dc.contributor.authorTrama, Annalisa-
dc.contributor.authorGatta, Gemma-
dc.contributor.authorBorràs Andrés, Josep Maria-
dc.contributor.authorCapocaccia, Ricardo-
dc.contributor.authorClèries Soler, Ramon-
dc.date.accessioned2022-07-19T12:29:41Z-
dc.date.available2022-07-19T12:29:41Z-
dc.date.issued2022-
dc.identifier.issn0002-9262-
dc.identifier.urihttp://hdl.handle.net/2445/187901-
dc.description.abstractEstimating 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.-
dc.format.extent12 p.-
dc.format.mimetypeapplication/pdf-
dc.language.isoeng-
dc.publisherOxford University Press-
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.1093/aje/kwab262-
dc.relation.ispartofAmerican Journal of Epidemiology, 2022, vol. 191, num. 3, p. 487-498-
dc.relation.urihttps://doi.org/10.1093/aje/kwab262-
dc.rightscc by (c) Salmerón, Diego et al., 2022-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.sourceArticles publicats en revistes (Ciències Clíniques)-
dc.subject.classificationCàncer-
dc.subject.classificationEstimació d'un paràmetre-
dc.subject.classificationEpidemiologia-
dc.subject.otherCancer-
dc.subject.otherParameter estimation-
dc.subject.otherEpidemiology-
dc.titleEstimating country-specific incidence rates of rare cancers: comparative perfomance analysis of modeling approaches using European cancer registry data-
dc.typeinfo:eu-repo/semantics/article-
dc.typeinfo:eu-repo/semantics/publishedVersion-
dc.identifier.idgrec716056-
dc.date.updated2022-07-19T12:29:41Z-
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess-
dc.identifier.pmid34718388-
Appears in Collections:Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))
Articles publicats en revistes (Ciències Clíniques)

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