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Hybrid model for recurrent event data

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dc.contributor.authorSousa-Ferreira, Ivo
dc.contributor.authorAbreu, Ana Maria
dc.date.accessioned2019-10-24T15:08:20Z
dc.date.available2019-10-24T15:08:20Z
dc.date.issued2019
dc.description.abstractIn the last four decades, there has been an increasing interest in developingsurvivalmodelsappropriateformultipleeventdataand,inparticular,for recurrentevent data. For these situations, several extensionsof the Cox’s regression model have been developed. Some of the most known models were suggested by: Prentice, Williams, and Peterson (PWP); Andersen and Gill (AG); Wei, Lin, and Weissfeld (WLW);andLee,Wei, andAmato(LWA).Thesemodelscanhandlewith situations where exist potentially correlated lifetimes of the same subject (due to the occurrence of more than one event for each subject) which is common in this type of data. In this chapter we present a new model, which we call hybrid model, with the purpose of minimizing some limitations of PWP model. With this model we obtained an improvement in the precision of the parameters estimates and a better fit to the simulated data.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSousa-Ferreira I., Abreu A.M. (2019) Hybrid Model for Recurrent Event Data. In: Ahmed S., Carvalho F., Puntanen S. (eds) Matrices, Statistics and Big Data. IWMS 2016. Contributions to Statistics. Springer, Cham, pp 23-33.pt_PT
dc.identifier.doi10.1007/978-3-030-17519-1_2pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/2580
dc.language.isoengpt_PT
dc.publisherSpringer Nature [academic journals on nature.com]pt_PT
dc.subjectCorrelated observationspt_PT
dc.subjectExtensions of Cox modelpt_PT
dc.subjectHybrid modelpt_PT
dc.subjectRecurrent eventspt_PT
dc.subjectSurvival analysispt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleHybrid model for recurrent event datapt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FMAT%2F04674%2F2013/PT
oaire.citation.endPage33pt_PT
oaire.citation.startPage23pt_PT
oaire.citation.titleMatrices, Statistics and Big Data. IWMS 2016. Contributions to Statisticspt_PT
oaire.fundingStream5876
person.familyNameSousa Ferreira
person.familyNameCortesão Pais Figueira da Silva Abreu
person.givenNameIvo Miguel
person.givenNameAna Maria
person.identifier.ciencia-id6E10-F64C-8B54
person.identifier.ciencia-id2013-43DF-3CB0
person.identifier.orcid0000-0001-5526-3594
person.identifier.orcid0000-0002-6155-8492
person.identifier.scopus-author-id57193736632
person.identifier.scopus-author-id55973527500
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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relation.isAuthorOfPublication237f47b0-8e6c-4e16-875e-cb59c862c6fb
relation.isAuthorOfPublication.latestForDiscovery834c0886-b3ef-4b47-8393-407e96d4bd50
relation.isProjectOfPublication046337fe-f827-4466-8e50-94fbf3be5607
relation.isProjectOfPublication.latestForDiscovery046337fe-f827-4466-8e50-94fbf3be5607

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