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Smooth time-dependent receiver operating characteristic curve estimators

dc.contributor.authorMartínez-Camblor P.
dc.contributor.authorPardo-Fernández J.C.
dc.date.accessioned2020-09-02T22:22:28Z
dc.date.available2020-09-02T22:22:28Z
dc.date.issued2018
dc.identifier10.1177/0962280217740786
dc.identifier.citation27, 3, 651-674
dc.identifier.issn09622802
dc.identifier.urihttps://hdl.handle.net/20.500.12728/5250
dc.descriptionThe receiver operating characteristic curve is a popular graphical method often used to study the diagnostic capacity of continuous (bio)markers. When the considered outcome is a time-dependent variable, two main extensions have been proposed: the cumulative/dynamic receiver operating characteristic curve and the incident/dynamic receiver operating characteristic curve. In both cases, the main problem for developing appropriate estimators is the estimation of the joint distribution of the variables time-to-event and marker. As usual, different approximations lead to different estimators. In this article, the authors explore the use of a bivariate kernel density estimator which accounts for censored observations in the sample and produces smooth estimators of the time-dependent receiver operating characteristic curves. The performance of the resulting cumulative/dynamic and incident/dynamic receiver operating characteristic curves is studied by means of Monte Carlo simulations. Additionally, the influence of the choice of the required smoothing parameters is explored. Finally, two real-applications are considered. An R package is also provided as a complement to this article. © 2017, © The Author(s) 2017.
dc.language.isoen
dc.publisherSAGE Publications Ltd
dc.subjectCensoring
dc.subjectdiscrimination
dc.subjectkernel density estimator
dc.subjectreceiver operating characteristic curve
dc.subjectsensitivity
dc.subjectspecificity
dc.subjectalbumin
dc.subjectbilirubin
dc.subjectbiological marker
dc.subjectbiological marker
dc.subjectage
dc.subjectaged
dc.subjectArticle
dc.subjectchronic obstructive lung disease
dc.subjectcohort analysis
dc.subjectcontrolled study
dc.subjectdiagnostic test accuracy study
dc.subjectfalse positive result
dc.subjectforced expiratory volume
dc.subjecthuman
dc.subjectkernel density estimator
dc.subjectkernel method
dc.subjectmajor clinical study
dc.subjectMonte Carlo method
dc.subjectmortality
dc.subjectprediction
dc.subjectprimary biliary cirrhosis
dc.subjectprothrombin time
dc.subjectreceiver operating characteristic
dc.subjectsensitivity and specificity
dc.subjectarea under the curve
dc.subjectbiostatistics
dc.subjectcomputer simulation
dc.subjectKaplan Meier method
dc.subjectprocedures
dc.subjectsoftware
dc.subjecttime factor
dc.subjectArea Under Curve
dc.subjectBiomarkers
dc.subjectBiostatistics
dc.subjectComputer Simulation
dc.subjectHumans
dc.subjectKaplan-Meier Estimate
dc.subjectMonte Carlo Method
dc.subjectROC Curve
dc.subjectSoftware
dc.subjectTime Factors
dc.titleSmooth time-dependent receiver operating characteristic curve estimators
dc.typeArticle


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