Bayesian regularization for flexible baseline hazard functions in Cox survival models

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dc.contributor.authorLazaro, Elena
dc.contributor.authorArmero, Carmen
dc.contributor.authorAlvares, Danilo
dc.date.accessioned2024-01-10T12:11:06Z
dc.date.available2024-01-10T12:11:06Z
dc.date.issued2021
dc.description.abstractFully Bayesian methods for Cox models specify a model for the baseline hazard function. Parametric approaches generally provide monotone estimations. Semi-parametric choices allow for more flexible patterns but they can suffer from overfitting and instability. Regularization methods through prior distributions with correlated structures usually give reasonable answers to these types of situations. We discuss Bayesian regularization for Cox survival models defined via flexible baseline hazards specified by a mixture of piecewise constant functions and by a cubic B-spline function. For those "semi-parametric" proposals, different prior scenarios ranging from prior independence to particular correlated structures are discussed in a real study with microvirulence data and in an extensive simulation scenario that includes different data sample and time axis partition sizes in order to capture risk variations. The posterior distribution of the parameters was approximated using Markov chain Monte Carlo methods. Model selection was performed in accordance with the deviance information criteria and the log pseudo-marginal likelihood. The results obtained reveal that, in general, Cox models present great robustness in covariate effects and survival estimates independent of the baseline hazard specification. In relation to the "semi-parametric" baseline hazard specification, the B-splines hazard function is less dependent on the regularization process than the piecewise specification because it demands a smaller time axis partition to estimate a similar behavior of the risk.
dc.description.funderSpanish Ministry of Economy and Competitiveness
dc.description.funderSpanish Ministry of Education, Culture and Sports
dc.fechaingreso.objetodigital02-04-2024
dc.format.extent20 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1002/bimj.201900211
dc.identifier.eissn1521-4036
dc.identifier.issn0323-3847
dc.identifier.pubmedidMEDLINE:32885493
dc.identifier.urihttps://doi.org/10.1002/bimj.201900211
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/76631
dc.identifier.wosidWOS:000565620000001
dc.information.autorucFacultad de Matemáticas; Alvares Da Silva, Danilo; 0000-0003-3764-0397; 1081962
dc.issue.numero1
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final26
dc.pagina.inicio7
dc.publisherWILEY
dc.revistaBIOMETRICAL JOURNAL
dc.rightsacceso restringido
dc.subjectcorrelated prior process
dc.subjectcubic B-splines
dc.subjectpiecewise functions
dc.subjectsurvival analysis
dc.subjectWeibull distribution
dc.subjectCOVARIANCE ANALYSIS
dc.subjectREGRESSION
dc.subjectDISTRIBUTIONS
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleBayesian regularization for flexible baseline hazard functions in Cox survival models
dc.typeartículo
dc.volumen63
sipa.codpersvinculados1081962
sipa.indexWOS
sipa.indexPubmed
sipa.trazabilidadCarga SIPA;09-01-2024
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