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dc.contributor.authorSerrat Piè, Carles
dc.contributor.authorRué, Montserrat
dc.contributor.authorArmero, Carmen
dc.contributor.authorPiulachs, Xavier
dc.contributor.authorPerpiñán Fabuel, Hèctor
dc.contributor.authorForte, Anabel
dc.contributor.authorPáez, Álvaro
dc.contributor.authorGómez Melis, Guadalupe
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Matemàtiques
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.date.accessioned2016-04-01T07:49:01Z
dc.date.available2016-04-01T07:49:01Z
dc.date.issued2015-06-03
dc.identifier.citationSerrat, C., Rué, M., Armero, C., Piulachs, X., Perpiñán, H., Forte, A., Páez, Á., Gomez, G. Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data. "Journal of Applied Statistics", 03 Juny 2015, vol. 42, núm. 6, p. 1223-1239.
dc.identifier.issn0266-4763
dc.identifier.urihttp://hdl.handle.net/2117/85035
dc.description.abstractThe paper describes the use of frequentist and Bayesian shared-parameter joint models of longitudinal measurements of prostate-specific antigen (PSA) and the risk of prostate cancer (PCa). The motivating dataset corresponds to the screening arm of the Spanish branch of the European Randomized Screening for Prostate Cancer study. The results show that PSA is highly associated with the risk of being diagnosed with PCa and that there is an age-varying effect of PSA on PCa risk. Both the frequentist and Bayesian paradigms produced very close parameter estimates and subsequent 95% confidence and credibility intervals. Dynamic estimations of disease-free probabilities obtained using Bayesian inference highlight the potential of joint models to guide personalized risk-based screening strategies.
dc.format.extent17 p.
dc.language.isoeng
dc.publisherTaylor & Francis
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
dc.subject.lcshSurvival analysis (Biometry)
dc.subject.other62P10
dc.subject.other62N01
dc.subject.otherrelative risk models
dc.subject.otherlinear mixed models
dc.subject.othershared-parameter models
dc.subject.otherprostate cancer screening
dc.subject.otherjoint models
dc.subject.otherTO-EVENT DATA
dc.subject.otherLATENT CLASS MODELS
dc.subject.otherDISEASE PROGRESSION
dc.subject.otherFOLLOW-UP
dc.subject.otherTIME
dc.subject.otherMORTALITY
dc.subject.otherSURVIVAL
dc.subject.otherFAILURE
dc.subject.otherPSA
dc.subject.otherPREDICTION
dc.titleFrequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data
dc.typeArticle
dc.subject.lemacAnàlisi de supervivència (Biometria)
dc.contributor.groupUniversitat Politècnica de Catalunya. GRBIO - Grup de Recerca en Bioestadística i Bioinformàtica
dc.identifier.doi10.1080/02664763.2014.999032
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
local.identifier.drac15536485
dc.description.versionPostprint (author's final draft)
local.citation.authorSerrat, C.; Rué, M.; Armero, C.; Piulachs, X.; Perpiñán, H.; Forte, A.; Páez, Á.; Gomez, G.
local.citation.publicationNameJournal of Applied Statistics
local.citation.volume42
local.citation.number6
local.citation.startingPage1223
local.citation.endingPage1239


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