{"data":{"id":"10.6084/m9.figshare.c.2118977.v1","type":"dois","attributes":{"doi":"10.6084/m9.figshare.c.2118977.v1","prefix":"10.6084","suffix":"m9.figshare.c.2118977.v1","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Jingjing Yin","affiliation":[],"nameIdentifiers":[]},{"name":"Tian, Lili","nameType":"Personal","givenName":"Lili","familyName":"Tian","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Generalized Inference Confidence Band for binormal ROC curve"}],"publisher":"Figshare","container":{},"publicationYear":2015,"subjects":[{"subject":"Cardiology"},{"subject":"Mathematics"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Biological Sciences"},{"subject":"Genetics"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Cell Biology"},{"subject":"Microbiology"},{"subject":"Medicine"},{"subject":"Engineering"},{"subject":"Biophysics"}],"contributors":[],"dates":[{"date":"2015-12-05","dateType":"Created"},{"date":"2015-12-05","dateType":"Updated"},{"date":"2015","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"Collection","resourceType":"Collection","resourceTypeGeneral":"Collection"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1080/19466315.2015.1093957","relatedIdentifierType":"DOI"},{"relationType":"IsPreviousVersionOf","relatedIdentifier":"10.6084/m9.figshare.c.2118977","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"CC-BY","rightsUri":"http://creativecommons.org/licenses/by/3.0/us"}],"descriptions":[{"description":"In medical practice, the diagnostic accuracy of a biomarker is usually measured by its sensitivity and specificity. The Receiver Operating Characteristic (\u003ci\u003eROC\u003c/i\u003e) curve is the graph of sensitivity against 1 − specificity as the cut-off point runs through all possible values. To account for sampling error and make inference about the true \u003ci\u003eROC\u003c/i\u003e curve, the simultaneous confidence band of the whole or partial \u003ci\u003eROC\u003c/i\u003e curve needs to be estimated across all values of specificity (can be within (0, 1) or some clinically-meaningful range). Particularly, for estimating the confidence band of the binormal \u003ci\u003eROC\u003c/i\u003e curve, there exists a Working-Hotelling type of method and the ellipse-envelop approach. However, these large-sample-based approaches do not provide satisfactory coverage for small to median samples. In this paper, we propose a new confidence band for the binormal \u003ci\u003eROC\u003c/i\u003e curve based on the generalized inference approach. Extensive simulation study is carried out to compare the performance of the proposed generalized confidence band with the existing large-sample-based confidence bands and a real data set is used to illustrate these methods. In conclusion, the proposed generalized confidence bands generally yield satisfactory coverage probabilities, while both large-sample-based confidence bands tend to be more liberal for most scenarios.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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