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Receiver operating characteristic curve generalization for non-monotone relationships
dc.contributor.author | Martínez-Camblor P. | |
dc.contributor.author | Corral N. | |
dc.contributor.author | Rey C. | |
dc.contributor.author | Pascual J. | |
dc.contributor.author | Cernuda-Morollón E. | |
dc.date.accessioned | 2020-09-02T22:22:28Z | |
dc.date.available | 2020-09-02T22:22:28Z | |
dc.date.issued | 2017 | |
dc.identifier | 10.1177/0962280214541095 | |
dc.identifier.citation | 26, 1, 113-123 | |
dc.identifier.issn | 09622802 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12728/5247 | |
dc.description | The 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.iso | en | |
dc.publisher | SAGE Publications Ltd | |
dc.subject | area under the curve | |
dc.subject | asymptotic distribution | |
dc.subject | receiver operating characteristic curve | |
dc.subject | resampling methods | |
dc.subject | botulinum toxin A | |
dc.subject | calcitonin gene related peptide | |
dc.subject | biological marker | |
dc.subject | botulinum toxin A | |
dc.subject | calcitonin gene related peptide | |
dc.subject | area under the curve | |
dc.subject | Article | |
dc.subject | critically ill patient | |
dc.subject | hemodialysis | |
dc.subject | hospital admission | |
dc.subject | human | |
dc.subject | leukocyte count | |
dc.subject | leukocytosis | |
dc.subject | measurement accuracy | |
dc.subject | Monte Carlo method | |
dc.subject | mortality risk | |
dc.subject | non monotone relationship | |
dc.subject | pediatric intensive care unit | |
dc.subject | phenotype | |
dc.subject | population research | |
dc.subject | probability | |
dc.subject | protein secretion | |
dc.subject | receiver operating characteristic | |
dc.subject | statistical analysis | |
dc.subject | transformed migraine | |
dc.subject | treatment response | |
dc.subject | area under the curve | |
dc.subject | blood | |
dc.subject | child | |
dc.subject | critical illness | |
dc.subject | female | |
dc.subject | migraine | |
dc.subject | mortality | |
dc.subject | sepsis | |
dc.subject | Area Under Curve | |
dc.subject | Biomarkers | |
dc.subject | Botulinum Toxins, Type A | |
dc.subject | Calcitonin Gene-Related Peptide | |
dc.subject | Child | |
dc.subject | Critical Illness | |
dc.subject | Female | |
dc.subject | Humans | |
dc.subject | Leukocyte Count | |
dc.subject | Migraine Disorders | |
dc.subject | Probability | |
dc.subject | ROC Curve | |
dc.subject | Sepsis | |
dc.title | Receiver operating characteristic curve generalization for non-monotone relationships | |
dc.type | Article |