Survival model induced by discrete frailty for modeling of lifetime data with long-term survivors and change-point

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dc.contributor.authorCancho, Vicente G.
dc.contributor.authorBarriga, Gladys [UNESP]
dc.contributor.authorLeao, Jeremias
dc.contributor.authorSaulo, Helton
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniv Fed Amazonas
dc.contributor.institutionUniversidade de Brasília (UnB)
dc.date.accessioned2019-10-04T12:15:18Z
dc.date.available2019-10-04T12:15:18Z
dc.date.issued2019-07-30
dc.description.abstractFrailty models are used for modeling heterogeneity in the data analysis of lifetimes. Analysis that ignore frailty when it is present leads to incorrect inferences. In survival analysis, the distribution of frailty is generally assumed to be continuous and, in some cases, it may be appropriate to consider a discrete frailty distribution. Survival models induced by frailty with a continuous distribution are not appropriate for situations in which survival data contain experimental units where the event of interest has not happened even after a long period of observation (survival data with cure fraction), that is, situations with units having zero frailty. In this paper, we propose a new survival model induced by discrete frailty for modeling survival data in the presence of a proportion of long-term survivors and a single change point. We use the maximum likelihood method to estimate the model parameters and evaluate their performance by a Monte Carlo simulation study. The proposed approach is illustrated by analyzing a kidney infection recurrence data set.en
dc.description.affiliationUniv Sao Paulo, Dept Math & Stat, Sao Carlos, SP, Brazil
dc.description.affiliationUniv Estadual Paulista, Dept Producing Engn, Sao Paulo, Brazil
dc.description.affiliationUniv Fed Amazonas, Dept Stat, Manaus, Amazonas, Brazil
dc.description.affiliationUniv Brasilia, Dept Stat, Brasilia, DF, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, Dept Producing Engn, Sao Paulo, Brazil
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipFAPEAM grants from the government of the State of Amazonas, Brazil
dc.format.extent12
dc.identifierhttp://dx.doi.org/10.1080/03610926.2019.1648826
dc.identifier.citationCommunications In Statistics-theory And Methods. Philadelphia: Taylor & Francis Inc, 12 p., 2019.
dc.identifier.doi10.1080/03610926.2019.1648826
dc.identifier.issn0361-0926
dc.identifier.urihttp://hdl.handle.net/11449/184626
dc.identifier.wosWOS:000480006100001
dc.language.isoeng
dc.publisherTaylor & Francis Inc
dc.relation.ispartofCommunications In Statistics-theory And Methods
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectChange-point hazard model
dc.subjectfrailty models
dc.subjectlong-term survivors
dc.subjectmaximum likelihood
dc.titleSurvival model induced by discrete frailty for modeling of lifetime data with long-term survivors and change-pointen
dc.typeArtigo
dcterms.licensehttp://journalauthors.tandf.co.uk/permissions/reusingOwnWork.asp
dcterms.rightsHolderTaylor & Francis Inc

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