Comparison of Bayesian survival analysis and Cox regression analysis in simulated and breast cancer data sets

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dc.contributor.authorOmurlu, Imran Kurt
dc.contributor.authorOzdamar, Kazim
dc.contributor.authorTure, Mevlut
dc.date.accessioned2024-06-12T10:58:50Z
dc.date.available2024-06-12T10:58:50Z
dc.date.issued2009
dc.departmentTrakya Üniversitesien_US
dc.description.abstractWe aimed to compare the performance of Cox regression analysis (CRA) and Bayesian survival analysis (BSA) by using simulations and breast cancer data. Simulation study was carried out with two different algorithms that were informative and noninformative priors. Moreover, in a real data set application, breast cancer data set related to disease-free survival (DFS) that was obtained from 423 breast cancer patients diagnosed between 1998 and 2007 was used. In the simulation application, it was observed that BSA with noninformative priors and CRA methods showed similar performances in point of convergence to simulation parameter. In the informative priors' simulation application, BSA with proper informative prior showed a good performance with too little bias. It was found out that the bias of BSA increased while priors were becoming distant from reliability in all sample sizes. In addition, BSA obtained predictions with more little bias and standard error than the CRA in both small and big samples in the light of proper priors. In the breast cancer data set, age, tumor size, hormonal therapy, and axillary nodal status were found statistically significant prognostic factors for DFS in stepwise CRA and BSA with informative and noninformative priors. Furthermore, standard errors of predictions in BSA with informative priors were observed slightly. As a result, BSA showed better performance than CRA, when subjective data analysis was performed by considering expert opinions and historical knowledge about parameters. Consequently, BSA should be preferred in existence of reliable informative priors, in the contrast cases, CRA should be preferred. (C) 2009 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2009.03.058
dc.identifier.endpage11346en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-67349233888en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage11341en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2009.03.058
dc.identifier.urihttps://hdl.handle.net/20.500.14551/20198
dc.identifier.volume36en_US
dc.identifier.wosWOS:000267179500060en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCox Regressionen_US
dc.subjectBayesian Survivalen_US
dc.subjectSurvivalen_US
dc.subjectBreast Canceren_US
dc.subjectMarkov Chain Monte Carloen_US
dc.subjectSimulationen_US
dc.subjectProportional Hazards Modelsen_US
dc.subjectCarcinomaen_US
dc.titleComparison of Bayesian survival analysis and Cox regression analysis in simulated and breast cancer data setsen_US
dc.typeArticleen_US

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