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dc.contributor.authorBarrio, I.
dc.contributor.authorArostegui, I. 
dc.contributor.authorRodríguez-Álvarez, M.X. 
dc.contributor.authorQuintana, J.M.
dc.date.accessioned2018-02-19T18:10:48Z
dc.date.available2018-02-19T18:10:48Z
dc.date.issued2017-12
dc.identifier.issn0962-2802
dc.identifier.urihttp://hdl.handle.net/20.500.11824/772
dc.description.abstractWhen developing prediction models for application in clinical practice, health practitioners usually categorise clinical variables that are continuous in nature. Although categorisation is not regarded as advisable from a statistical point of view, due to loss of information and power, it is a common practice in medical research. Consequently, providing researchers with a useful and valid categorisation method could be a relevant issue when developing prediction models. Without recommending categorisation of continuous predictors, our aim is to propose a valid way to do it whenever it is considered necessary by clinical researchers. This paper focuses on categorising a continuous predictor within a logistic regression model, in such a way that the best discriminative ability is obtained in terms of the highest area under the receiver operating characteristic curve (AUC). The proposed methodology is validated when the optimal cut points' location is known in theory or in practice. In addition, the proposed method is applied to a real data set of patients with an exacerbation of chronic obstructive pulmonary disease, in the context of the IRYSS-COPD study where a clinical prediction rule for severe evolution was being developed. The clinical variable PCO2 was categorised in a univariable and a multivariable setting.en_US
dc.description.sponsorshipMINECO: MTM2010-14913, MTM2011-28285-C02-01 and MTM2013-40941-P. Basque Government: IT620-13, 2012111008. University of the Basque Country UPV/EHU: GIU10/21, UFI11/52. Agrupamento IN-BIOMED from DXPCTSUG-FEDER unha maneira de facer Europa (2012/273). Red IRYSS (Investigación en Resultados y Servicios Sanitarios)- of the Instituto de Salud Carlos III: G03/220en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectcategorisationen_US
dc.subjectprediction modelsen_US
dc.subjectcut pointen_US
dc.subjectvalidationen_US
dc.titleA new approach to categorize continuous variables in prediction models: Proposal and validationen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doi10.1177/0962280215601873
dc.relation.publisherversionhttp://journals.sagepub.com/doi/full/10.1177/0962280215601873en_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersionen_US
dc.journal.titleStatistical Methods in Medical Researchen_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España