{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T00:47:42Z","timestamp":1792111662736,"version":"4.4.0"},"reference-count":26,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2013,11,21]],"date-time":"2013-11-21T00:00:00Z","timestamp":1384992000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biometrics"],"published-print":{"date-parts":[[2014,3]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:sec>\n                    <jats:label\/>\n                    <jats:p>After establishing the utility of a continuous diagnostic marker investigators will typically address the question of determining a cut\u2010off point which will be used for diagnostic purposes in clinical decision making. The most commonly used optimality criterion for cut\u2010off point selection in the context of ROC curve analysis is the maximum of the Youden index. The pair of sensitivity and specificity proportions that correspond to the Youden index\u2010based cut\u2010off point characterize the performance of the diagnostic marker. Confidence intervals for sensitivity and specificity are routinely estimated based on the assumption that sensitivity and specificity are independent binomial proportions as they arise from the independent populations of diseased and healthy subjects, respectively. The Youden index\u2010based cut\u2010off point is estimated from the data and as such the resulting sensitivity and specificity proportions are in fact correlated. This correlation needs to be taken into account in order to calculate confidence intervals that result in the anticipated coverage. In this article we study parametric and non\u2010parametric approaches for the construction of confidence intervals for the pair of sensitivity and specificity proportions that correspond to the Youden index\u2010based optimal cut\u2010off point. These approaches result in the anticipated coverage under different scenarios for the distributions of the healthy and diseased subjects. We find that a parametric approach based on a Box\u2013Cox transformation to normality often works well. For biomarkers following more complex distributions a non\u2010parametric procedure using logspline density estimation can be used.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/biom.12107","type":"journal-article","created":{"date-parts":[[2013,11,21]],"date-time":"2013-11-21T17:22:24Z","timestamp":1385054544000},"page":"212-223","source":"Crossref","is-referenced-by-count":126,"title":["Construction of confidence regions in the ROC space after the estimation of the optimal Youden index\u2010based cut\u2010off point"],"prefix":"10.1093","volume":"70","author":[{"given":"Leonidas E.","family":"Bantis","sequence":"first","affiliation":[{"name":"Department of Statistics and Actuarial\u2010Financial Mathematics University of the Aegean 83200 Samos Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christos T.","family":"Nakas","sequence":"additional","affiliation":[{"name":"Laboratory of Biometry University of Thessaly Phytokou Street, 38446 Volos Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Reiser","sequence":"additional","affiliation":[{"name":"Department of Statistics University of Haifa Haifa 31905 Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2013,11,21]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00431-010-1330-4"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.200410135"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-5877(00)00115-X"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0258(19960730)15:14<1575::AID-SIM283>3.0.CO;2-2"},{"key":"e_1_2_8_6_1","first-page":"1371","article-title":"The use of polynomial splines and their tensor products in extended linear modeling (with discussion)","volume":"25","author":"Hansen M.","year":"1997","journal-title":"The Annals of Statistics"},{"key":"e_1_2_8_7_1","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1080\/01621459.1980.10477563","article-title":"The large sample behavior of transformations to normality","volume":"75","author":"Hernandez F.","year":"1980","journal-title":"Journal of the American Statistical Association"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/0167-9473(91)90115-I"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1198\/1061860043047"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.3899"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2012.09.004"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.4303"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.200610223"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.1464"},{"key":"e_1_2_8_15_1","doi-asserted-by":"crossref","unstructured":"Pepe M. 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