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dc.contributor.authorMartínez-Camblor P.
dc.contributor.authorCorral N.
dc.contributor.authorRey C.
dc.contributor.authorPascual J.
dc.contributor.authorCernuda-Morollón E.
dc.date.accessioned2020-09-02T22:22:28Z
dc.date.available2020-09-02T22:22:28Z
dc.date.issued2017
dc.identifier10.1177/0962280214541095
dc.identifier.citation26, 1, 113-123
dc.identifier.issn09622802
dc.identifier.urihttps://hdl.handle.net/20.500.12728/5247
dc.descriptionThe receiver operating characteristic curve is a popular graphical method frequently used in order to study the diagnostic capacity of continuous markers. It represents in a plot true-positive rates against the false-positive ones. Both the practical and theoretical aspects of the receiver operating characteristic curve have been extensively studied. Conventionally, it is assumed that the considered marker has a monotone relationship with the studied characteristic; i.e., the upper (lower) values of the (bio)marker are associated with a higher probability of a positive result. However, there exist real situations where both the lower and the upper values of the marker are associated with higher probability of a positive result. We propose a receiver operating characteristic curve generalization, g, useful in this context. All pairs of possible cut-off points, one for the lower and another one for the upper marker values, are taken into account and the best of them are selected. The natural empirical estimator for the g curve is considered and its uniform consistency and asymptotic distribution are derived. Finally, two real-world applications are studied. © The Author(s) 2014.
dc.language.isoen
dc.publisherSAGE Publications Ltd
dc.subjectarea under the curve
dc.subjectasymptotic distribution
dc.subjectreceiver operating characteristic curve
dc.subjectresampling methods
dc.subjectbotulinum toxin A
dc.subjectcalcitonin gene related peptide
dc.subjectbiological marker
dc.subjectbotulinum toxin A
dc.subjectcalcitonin gene related peptide
dc.subjectarea under the curve
dc.subjectArticle
dc.subjectcritically ill patient
dc.subjecthemodialysis
dc.subjecthospital admission
dc.subjecthuman
dc.subjectleukocyte count
dc.subjectleukocytosis
dc.subjectmeasurement accuracy
dc.subjectMonte Carlo method
dc.subjectmortality risk
dc.subjectnon monotone relationship
dc.subjectpediatric intensive care unit
dc.subjectphenotype
dc.subjectpopulation research
dc.subjectprobability
dc.subjectprotein secretion
dc.subjectreceiver operating characteristic
dc.subjectstatistical analysis
dc.subjecttransformed migraine
dc.subjecttreatment response
dc.subjectarea under the curve
dc.subjectblood
dc.subjectchild
dc.subjectcritical illness
dc.subjectfemale
dc.subjectmigraine
dc.subjectmortality
dc.subjectsepsis
dc.subjectArea Under Curve
dc.subjectBiomarkers
dc.subjectBotulinum Toxins, Type A
dc.subjectCalcitonin Gene-Related Peptide
dc.subjectChild
dc.subjectCritical Illness
dc.subjectFemale
dc.subjectHumans
dc.subjectLeukocyte Count
dc.subjectMigraine Disorders
dc.subjectProbability
dc.subjectROC Curve
dc.subjectSepsis
dc.titleReceiver operating characteristic curve generalization for non-monotone relationships
dc.typeArticle


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