A correlated Bayesian rank likelihood approach to multiple ROC curves for endometriosis
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chen, Z. | - |
dc.contributor.author | Hwang, B.S. | - |
dc.contributor.author | Kim, S. | - |
dc.date.available | 2019-03-08T06:56:27Z | - |
dc.date.issued | 2019-04 | - |
dc.identifier.issn | 0277-6715 | - |
dc.identifier.issn | 1097-0258 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/3261 | - |
dc.description.abstract | In analysis of diagnostic data with multiple tests, it is often the case that these tests are correlated. Modeling the correlation explicitly not only produces valid inference results but also enables borrowing of information. Motivated by the Physician Reliability Study (PRS) that investigated the diagnostic performance of physicians in diagnosing endometriosis, we construct a correlated modeling framework to estimate ROC curves and the associated area under the curves. This correlated approach is quite appealing for the PRS data set that suffers from the problem of small sample sizes, as it enables information borrowing between physician groups and sessions. Given that the test scores appear to be non-normal even after logarithm transformation, we use the ranks of the data to conduct likelihood estimation and inference. We use the deviance information criterion to select competing models and conduct simulation studies to assess model performances. In application to the PRS data set, we found that the physicians are not significantly different in their diagnostic performance between groups; however, they are different between the sessions. This suggests that clinical information may play a more important role in physicians' diagnostic performance than their experiences. Our empirical evidence also demonstrates that when using both woman- and physician-specific random effects, the model parameter estimates are much smoother. © 2018 John Wiley & Sons, Ltd. | - |
dc.format.extent | 12 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | John Wiley and Sons Ltd | - |
dc.title | A correlated Bayesian rank likelihood approach to multiple ROC curves for endometriosis | - |
dc.type | Article | - |
dc.identifier.doi | 10.1002/sim.8043 | - |
dc.identifier.bibliographicCitation | Statistics in Medicine, v.38, no.8, pp 1374 - 1385 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000460319700006 | - |
dc.identifier.scopusid | 2-s2.0-85056345301 | - |
dc.citation.endPage | 1385 | - |
dc.citation.number | 8 | - |
dc.citation.startPage | 1374 | - |
dc.citation.title | Statistics in Medicine | - |
dc.citation.volume | 38 | - |
dc.type.docType | Article in Press | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | couple-based study | - |
dc.subject.keywordAuthor | environmental pollutants | - |
dc.subject.keywordAuthor | kernel machine regression | - |
dc.subject.keywordAuthor | nonparametric regression | - |
dc.subject.keywordAuthor | REML | - |
dc.subject.keywordPlus | INFORMATION CRITERIA | - |
dc.subject.keywordPlus | INFERENCE | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalResearchArea | Public, Environmental & Occupational Health | - |
dc.relation.journalResearchArea | Medical Informatics | - |
dc.relation.journalResearchArea | Research & Experimental Medicine | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Public, Environmental & Occupational Health | - |
dc.relation.journalWebOfScienceCategory | Medical Informatics | - |
dc.relation.journalWebOfScienceCategory | Medicine, Research & Experimental | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.description.journalRegisteredClass | sci | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.