Bayesian variable selection and survival modeling : assessing the most important comorbidities that impact lung and colorectal cancer survival in Spain

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dc.contributor.authorRubio, Francisco J.
dc.contributor.authorSilva, Danilo Alvares da
dc.contributor.authorRedondo-Sanchez, Daniel
dc.contributor.authorMarcos-Gragera, Rafael
dc.contributor.authorSánchez, María-José
dc.contributor.authorLuque-Fernandez, Miguel A.
dc.date.accessioned2022-05-02T14:08:54Z
dc.date.available2022-05-02T14:08:54Z
dc.date.issued2022
dc.date.updated2022-04-10T00:03:14Z
dc.description.abstractCancer survival represents one of the main indicators of interest in cancer epidemiology. However, the survival of cancer patients can be affected by several factors, such as comorbidities, that may interact with the cancer biology. Moreover, it is interesting to understand how different cancer sites and tumour stages are affected by different comorbidities. Identifying the comorbidities that affect cancer survival is thus of interest as it can be used to identify factors driving the survival of cancer patients. This information can also be used to identify vulnerable groups of patients with comorbidities that may lead to worst prognosis of cancer. We address these questions and propose a principled selection and evaluation of the effect of comorbidities on the overall survival of cancer patients. In the first step, we apply a Bayesian variable selection method that can be used to identify the comorbidities that predict overall survival. In the second step, we build a general Bayesian survival model that accounts for time-varying effects. In the third step, we derive several posterior predictive measures to quantify the effect of individual comorbidities on the population overall survival. We present applications to data on lung and colorectal cancers from two Spanish population-based cancer registries. The proposed methodology is implemented with a combination of the R-packages mombf and rstan. We provide the code for reproducibility at https://github.com/migariane/BayesVarImpComorbiCancer .
dc.fuente.origenAutoarchivo
dc.identifier.citationBMC Medical Research Methodology. 2022 Apr 03;22(1):95
dc.identifier.urihttps://doi.org/10.1186/s12874-022-01582-0
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/63706
dc.information.autorucFacultad de Matemáticas ; Silva, Danilo Alvares da ; 0000-0003-3764-0397 ; 1081962
dc.issue.numero95
dc.language.isoen
dc.nota.accesoContenido completo
dc.pagina.final14
dc.pagina.inicio1
dc.relation.isformatofBMC Medical Research Methodology, vol. 22, no. 95 (2022:apr.)
dc.revistaBMC Medical Research Methodology
dc.rightsacceso abierto
dc.rights.holderThe Author(s)
dc.subjectBayesian variable selectiones_ES
dc.subjectCancer survivales_ES
dc.subjectComorbiditieses_ES
dc.subjectConditional effectses_ES
dc.subjectMarginal effectses_ES
dc.subject.ddc500
dc.subject.deweyCienciases_ES
dc.subject.otherTeoría bayesiana de decisiones estadísticases_ES
dc.subject.otherCáncer - Casos clínicoses_ES
dc.subject.otherMorbilidad - Estadísticases_ES
dc.titleBayesian variable selection and survival modeling : assessing the most important comorbidities that impact lung and colorectal cancer survival in Spaines_ES
dc.typeartículo
dc.volumen22
sipa.codpersvinculados1081962
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