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dc.contributor.authorBarrio, Irantzu
dc.contributor.authorRodríguez-Álvarez, María Xosé
dc.contributor.authorMeira-Machado, Luis
dc.contributor.authorEsteban, Cristóbal
dc.contributor.authorArostegui, Inmaculada
dc.identifier.citationBarrio, I. [et al.]. Comparison of two discrimination indexes in the categorisation of continuous predictors in time-to-event studies. "SORT", 21 Juny 2017, vol. 1, p. 73-92.
dc.description.abstractThe Cox proportionalhazards model is the most widely used su rvival prediction model for analysing time-to-event data. To measure the discrimination ability of a survival model the concordance probability index is widely used. In this work we studied and compared the performance of two different estimators of the concordance probability when a continuous predictor variable is cate- gorised in a Cox proportional hazards regression model. In p articular, we compared the c-index and the concordance probability estimator. We evaluated th e empirical performance of both es- timators through simulations. To categorise the predictor variable we propose a methodology which considers the maximal discrimination attained for th e categorical variable. We applied this methodology to a cohort of patients with chronic obstructiv e pulmonary disease, in particular, we categorised the predictor variable forced expiratory volu me in one second in percentage
dc.format.extent20 p.
dc.publisherInstitut d'Estadística de Catalunya
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
dc.subject.otherprediction models
dc.subject.otherCox model
dc.titleComparison of two discrimination indexes in the categorisation of continuous predictors in time-to-event studies
dc.description.peerreviewedPeer Reviewed
dc.subject.amsClassificació AMS::62 Statistics::62N Survival analysis and censored data
dc.subject.amsClassificació AMS::62 Statistics::62P Applications
dc.rights.accessOpen Access

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Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain