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dc.contributor.authorBarrio, I.
dc.contributor.authorArostegui, I. 
dc.contributor.authorRodríguez-Álvarez, M.X. 
dc.date.accessioned2018-02-19T18:12:27Z
dc.date.available2018-02-19T18:12:27Z
dc.date.issued2017-12
dc.identifier.isbn978-3-319-55639-0
dc.identifier.urihttp://hdl.handle.net/20.500.11824/773
dc.description.abstractRecent advances in information technologies are generating a growth in the amount of available biomedical data. In this paper, we studied the impact sample size may have on the categorisation of a continuous predictor variable in a logistic regression setting. Two different approaches to categorise predictor variables were compared.en_US
dc.description.sponsorshipMINECO: MTM2011-28285-C02-01, MTM2013-40941-P, MTM2014-55966-P. Basque Government: IT620-13. University of the Basque Country UPV/EHU: UFI11/52 Agrupamento INBIOMED from DXPCTSUG-FEDER unha maneira de facer Europa (2012/273).en_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.subjectsample sizeen_US
dc.subjectcategorisationen_US
dc.titleSample size impact on the categorisation of continuous variables in clinical predictionen_US
dc.typeinfo:eu-repo/semantics/bookParten_US
dc.relation.publisherversionhttps://link.springer.com/book/10.1007%2F978-3-319-55639-0en_US
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen_US
dc.journal.titleTrends in Mathematics. Research Perspectives CRM Barcelona. Extended Abstracts Fall 2015. Biomedical Big Data; Statistics for Low Dose Radiation 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