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Efficient nonparametric confidence bands for receiver operating-characteristic curves
dc.contributor.author | Martínez-Camblor P. | |
dc.contributor.author | Pérez-Fernández S. | |
dc.contributor.author | Corral N. | |
dc.date.accessioned | 2020-09-02T22:22:29Z | |
dc.date.available | 2020-09-02T22:22:29Z | |
dc.date.issued | 2018 | |
dc.identifier | 10.1177/0962280216672490 | |
dc.identifier.citation | 27, 6, 1892-1908 | |
dc.identifier.issn | 09622802 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12728/5251 | |
dc.description | Receiver operating-characteristic curve is a popular graphical method frequently used in order to study the diagnostic capacity of continuous (bio)markers. In spite of the existence of a huge number of papers devoted to both theoretical and practical aspects of this topic, the construction of confidence bands has had little impact in the specialized literature. As far as the authors know, in the CRAN there are only three R packages providing receiver operating-characteristic curve confidence regions: plotROC, pROC and fbroc. This work tries to fill this gap studying and proposing a new nonparametric method to build confidence bands for both the standard receiver operating-characteristic curve and its generalization for nonmonotone relationships. The behavior of the proposed procedure is studied via Monte Carlo simulations and the methodology is applied on two real-world biomedical problems. In addition, an R function to compute the proposed and some of the previously existing methodologies is provided as online supplementary material. © 2016, © The Author(s) 2016. | |
dc.language.iso | en | |
dc.publisher | SAGE Publications Ltd | |
dc.subject | bootstrap method | |
dc.subject | Confidence bands | |
dc.subject | receiver operating-characteristic curve | |
dc.subject | sensitivity | |
dc.subject | specificity | |
dc.subject | Article | |
dc.subject | bootstrapping | |
dc.subject | chronic hepatitis C | |
dc.subject | confidence interval | |
dc.subject | human | |
dc.subject | Monte Carlo method | |
dc.subject | nonparametric test | |
dc.subject | postoperative infection | |
dc.subject | predictive value | |
dc.subject | receiver operating characteristic | |
dc.subject | sensitivity and specificity | |
dc.subject | treatment outcome | |
dc.subject | algorithm | |
dc.subject | chronic hepatitis B | |
dc.subject | diagnostic test | |
dc.subject | drug effect | |
dc.subject | Hepatitis B virus | |
dc.subject | nonparametric test | |
dc.subject | outcome assessment | |
dc.subject | biological marker | |
dc.subject | Algorithms | |
dc.subject | Biomarkers | |
dc.subject | Confidence Intervals | |
dc.subject | Diagnostic Tests, Routine | |
dc.subject | Hepatitis B virus | |
dc.subject | Hepatitis B, Chronic | |
dc.subject | Humans | |
dc.subject | Outcome Assessment (Health Care) | |
dc.subject | ROC Curve | |
dc.subject | Statistics, Nonparametric | |
dc.title | Efficient nonparametric confidence bands for receiver operating-characteristic curves | |
dc.type | Article |