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dc.contributor.authorCarvalho, Vanda Inácio de
dc.contributor.authorCarvalho, Miguel de
dc.contributor.authorAlonzo, Todd A.
dc.contributor.authorGonzález Manteiga, Wenceslao
dc.date.accessioned2019-04-09T08:18:00Z
dc.date.available2019-04-09T08:18:00Z
dc.date.issued2016
dc.identifier.citationInácio de Carvalho, Vanda; de Carvalho, Miguel; Alonzo, Todd A.; González-Manteiga, Wenceslao. Functional covariate-adjusted partial area under the specificity-ROC curve with an application to metabolic syndrome diagnosis. Ann. Appl. Stat. 10 (2016), no. 3, 1472-1495. doi:10.1214/16-AOAS943
dc.identifier.issn1932-6157
dc.identifier.urihttp://hdl.handle.net/10347/18566
dc.description.abstractDue to recent advances in technology, medical diagnosis data are becoming increasingly complex and, nowadays, applications where measurements are curves or images are ubiquitous. Motivated by the need of modeling a functional covariate on a metabolic syndrome case study, we develop a nonparametric functional regression model for the area under the specificity receiver operating characteristic curve. This partial area is a meaningful summary measure of diagnostic accuracy for cases in which misdiagnosis of diseased subjects may lead to serious clinical consequences, and hence it is critical to maintain a high sensitivity. Its normalized value can be interpreted as the average specificity over the interval of sensitivities considered, thus summarizing the trade-off between sensitivity and specificity. Our methods are motivated by, and applied to, a metabolic syndrome study that investigates how restricting the sensitivity of the gamma-glutamyl-transferase, a metabolic syndrome marker, to certain clinical meaningful values, affects its corresponding specificity and how it might change for different curves of arterial oxygen saturation. Application of our methods suggests that oxygen saturation is key to gamma-glutamyl transferase’s performance and that some of the different intervals of sensitivities considered offer a good tradeoff between sensitivity and specificity. The simulation study shows that the estimator associated with our model is able to recover successfully the true overall shape of the functional covariate-adjusted partial area under the curve in different complex scenarios
dc.description.sponsorshipPartially funded by Fondecyt Grants 11130541 (first author) and 11121186 (second author). Supported in part by the Spanish Ministry of Science and Innovation through project MTM2008-03010
dc.language.isoeng
dc.publisherThe Institute of Mathematical Statistics
dc.relationinfo:eu-repo/grantAgreement/MICINN/Plan Nacional de I+D+i 2008-2011/MTM2008-03010/ES/METODOLOGIA Y APLICACIONES EN ESTADISTICA SEMIPARAMETRICA, FUNCIONAL Y ESPACIO TEMPORAL
dc.rights© Institute of Mathematical Statistics, 2016
dc.subjectArterial oxygen saturation
dc.subjectAverage specificity
dc.subjectBiomarker
dc.subjectFunctional covariate-adjustment
dc.subjectGamma-glutamyl transferase
dc.subjectKernel regression
dc.subjectMetabolic syndrome
dc.subjectPartial area under the curve
dc.subjectSensitivity
dc.subjectSpecificity-receiver operating characteristic curve
dc.titleFunctional covariate-adjusted partial area under the specificity-ROC curve with an application to metabolic syndrome diagnosis
dc.typeinfo:eu-repo/semantics/article
dc.identifier.DOI10.1214/16-AOAS943
dc.relation.publisherversionhttps://doi.org/10.1214/16-AOAS943
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.e-issn1941-7330
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Estatística, Análise Matemática e Optimización
dc.description.peerreviewedSI


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