Optimal cutoff points for classification in diagnostic studies: new contributions and software development

dc.contributor.advisorCadarso Suárez, Carmen María
dc.contributor.advisorMolanes López, Elisa María
dc.contributor.authorLópez Ratón, Mónica
dc.contributor.otherUniversidade de Santiago de Compostela. Facultade de Matemáticas. Departamento de Estatística e Investigación Operativa
dc.date.accessioned2016-04-15T07:47:07Z
dc.date.available2016-04-15T07:47:07Z
dc.date.issued2016-04-15
dc.description.abstractContinuous diagnostic tests (biomarkers or risk markers) are often used to discriminate between healthy and diseased populations. For the clinical application of such tests, the key aspect is how to select an appropriate cutpoint or discrimination value c that defines positive and negative test results. In general, individuals with a diagnostic test value smaller than c are classified as healthy and otherwise as diseased. In the literature, several methods have been proposed to select the threshold value c in terms of different specific criteria of optimality. Among others, one of the methods most used in clinical practice is the Symmetry point that maximizes simultaneously both types of correct classifications. From a graphical viewpoint, the Symmetry point is associated to the operating point on the Receiver Operating Characteristic (ROC) curve that intersects the diagonal line passing through the points (0,1) and (1,0). However, this cutpoint is actually valid only when the error of misclassifying a diseased patient has the same severity than the error of misclassifying a healthy patient. Since this may not be the case in practice, an important issue in order to assess the clinical effectiveness of a biomarker is to take into account the costs associated with the decisions taken when selecting the threshold value. Moreover, to facilitate the task of selecting the optimal cut-off point in clinical practice, it is essential to have software that implements the existing optimal criteria in an user-friendly environment. Another interesting issue appears when the marker shows an irregular distribution, with a dominance of diseased subjects in noncontiguous regions. Using a single cutpoint, as common practice in traditional ROC analysis, would not be appropriate for these scenarios because it would lead to erroneous conclusions, not taking full advantage of the intrinsic classificatory capacity of the marker.gl
dc.identifier.urihttp://hdl.handle.net/10347/14593
dc.language.isoenggl
dc.rightsEsta obra atópase baixo unha licenza internacional Creative Commons BY-NC-ND 4.0. Calquera forma de reprodución, distribución, comunicación pública ou transformación desta obra non incluída na licenza Creative Commons BY-NC-ND 4.0 só pode ser realizada coa autorización expresa dos titulares, salvo excepción prevista pola lei. Pode acceder Vde. ao texto completo da licenza nesta ligazón: https://creativecommons.org/licenses/by-nc-nd/4.0/deed.gl
dc.rights.accessRightsopen accessgl
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.gl
dc.subjectBiomarkergl
dc.subjectDiagnostic testgl
dc.subjectEmpirical Likelihoodgl
dc.subjectGeneralized Pivotal Quantitygl
dc.subjectLogistic GAM regression modelgl
dc.subjectMisclassification costsgl
dc.subjectOptimal thresholdgl
dc.subjectROC curvegl
dc.subjectR packagegl
dc.subjectSymmetry pointgl
dc.subject.classificationMaterias::Investigación::12 Matemáticas::1209 Estadística::120903 Análisis de datosgl
dc.subject.classificationMaterias::Investigación::12 Matemáticas::1207 Investigación operativa::120702 Sistemas de controlgl
dc.subject.classificationMaterias::Investigación::24 Ciencias de la vida::2404 Biomatemáticas::240401 Bioestadísticagl
dc.titleOptimal cutoff points for classification in diagnostic studies: new contributions and software developmentgl
dc.typedoctoral thesisgl
dspace.entity.typePublication
relation.isAdvisorOfPublication75edf723-9599-41be-b0dd-e365144993e0
relation.isAdvisorOfPublication.latestForDiscovery75edf723-9599-41be-b0dd-e365144993e0

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