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dc.rights.licenseopenen_US
dc.contributor.authorBLANCHE, Paul
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorDARTIGUES, Jean-Francois
ORCID: 0000-0001-9482-5529
IDREF: 058586105
dc.contributor.authorRIOU, Jeremie
dc.date.accessioned2022-04-25T08:42:32Z
dc.date.available2022-04-25T08:42:32Z
dc.date.issued2022-03
dc.identifier.issn1541-0420 (Electronic) 0006-341X (Linking)en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/139918
dc.description.abstractEnComparing areas under the ROC curve (AUCs) is a popular approach to compare prognostic biomarkers. The aim of this paper is to present an efficient method to control the family-wise error rate when multiple comparisons are performed. We suggest to combine the max-t test and the closed testing procedures. We build on previous work on asymptotic results for ROC curves and on general multiple testing methods to efficiently take into account both the correlations between the test statistics and the logical constraints between the null hypotheses. The proposed method results in an uniformly more powerful procedure than both the single-step max-t test procedure and popular stepwise extensions of the Bonferroni procedure, such as Bonferroni-Holm. As demonstrated in this paper, the method can be applied in most usual contexts, including the time-dependent context with right censored data. We show how the method works in practice through a motivating example where we compare several psychometric scores to predict the t-year risk of Alzheimer's disease. The example illustrates several multiple testing settings and demonstrates the advantage of using the proposed methods over common alternatives. R code has been made available to facilitate the use of the methods by others.
dc.language.isoENen_US
dc.subject.enBiomarker
dc.subject.enClosed testing
dc.subject.enMax-t test
dc.subject.enMultiple testing
dc.subject.enROC curve
dc.subject.enSurvival analysis
dc.title.enA closed max-t test for multiple comparisons of areas under the ROC curve
dc.typeArticle de revueen_US
dc.identifier.doi10.1111/biom.13401en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed33207001en_US
bordeaux.journalBiometricsen_US
bordeaux.page352-363en_US
bordeaux.volume78en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.issue1en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamACTIVE_BPHen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.exportfalse
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Biometrics&rft.date=2022-03&rft.volume=78&rft.issue=1&rft.spage=352-363&rft.epage=352-363&rft.eissn=1541-0420%20(Electronic)%200006-341X%20(Linking)&rft.issn=1541-0420%20(Electronic)%200006-341X%20(Linking)&rft.au=BLANCHE,%20Paul&DARTIGUES,%20Jean-Francois&RIOU,%20Jeremie&rft.genre=article


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