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http://hdl.handle.net/1942/21510
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DC Field | Value | Language |
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dc.contributor.author | GARCIA BARRADO, Leandro | - |
dc.contributor.author | Coart, Els | - |
dc.contributor.author | BURZYKOWSKI, Tomasz | - |
dc.date.accessioned | 2016-06-10T13:33:29Z | - |
dc.date.available | 2016-06-10T13:33:29Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | STATISTICS IN MEDICINE, 35 (4), p. 595-608 | - |
dc.identifier.issn | 0277-6715 | - |
dc.identifier.uri | http://hdl.handle.net/1942/21510 | - |
dc.description.abstract | Ignoring the fact that the reference test used to establish the discriminative properties of a combination of diagnostic biomarkers is imperfect can lead to a biased estimate of the diagnostic accuracy of the combination. In this paper, we propose a Bayesian latent-class mixture model to select a combination of biomarkers that maximizes the area under the ROC curve (AUC), while taking into account the imperfect nature of the reference test. In particular, a method for specification of the prior for the mixture component parameters is developed that allows controlling the amount of prior information provided for the AUC. The properties of the model are evaluated by using a simulation study and an application to real data from Alzheimer's disease research. In the simulation study, 100 data sets are simulated for sample sizes ranging from 100 to 600 observations, with a varying correlation between biomarkers. The inclusion of an informative as well as a flat prior for the diagnostic accuracy of the reference test is investigated. In the real-data application, the proposed model was compared with the generally used logistic-regression model that ignores the imperfectness of the reference test. Conditional on the selected sample size and prior distributions, the simulation study results indicate satisfactory performance of the model-based estimates. In particular, the obtained average estimates for all parameters are close to the true values. For the real-data application, AUC estimates for the proposed model are substantially higher than those from the 'traditional' logistic-regression model. Copyright (C) 2015 John Wiley & Sons, Ltd. | - |
dc.description.sponsorship | Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer's Association; Alzheimer's Drug Discovery Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuroimaging at the University of Southern California. | - |
dc.language.iso | en | - |
dc.publisher | WILEY-BLACKWELL | - |
dc.rights | Copyright © 2015 John Wiley & Sons, Ltd. | - |
dc.subject.other | Bayesian estimation; biomarkers; latent-class mixture models; AUC | - |
dc.subject.other | Bayesian estimation; Biomarkers; Latent-class mixture models; AUC | - |
dc.title | Development of a diagnostic test based on multiple continuous biomarkers with an imperfect reference test | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 608 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 595 | - |
dc.identifier.volume | 35 | - |
local.format.pages | 14 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | [Barrado, Leandro Garcia; Burzykowski, Tomasz] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat I BioSt, Agoralaan Bldg D, B-3590 Diepenbeek, Belgium. [Coart, Els; Burzykowski, Tomasz] Int Inst Drug Dev, Ave Provinciale 30, B-1340 Louvain La Neuve, Belgium. | - |
local.publisher.place | HOBOKEN | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1002/sim.6733 | - |
dc.identifier.isi | 000368944800012 | - |
item.fullcitation | GARCIA BARRADO, Leandro; Coart, Els & BURZYKOWSKI, Tomasz (2016) Development of a diagnostic test based on multiple continuous biomarkers with an imperfect reference test. In: STATISTICS IN MEDICINE, 35 (4), p. 595-608. | - |
item.validation | ecoom 2017 | - |
item.fulltext | With Fulltext | - |
item.accessRights | Restricted Access | - |
item.contributor | GARCIA BARRADO, Leandro | - |
item.contributor | Coart, Els | - |
item.contributor | BURZYKOWSKI, Tomasz | - |
crisitem.journal.issn | 0277-6715 | - |
crisitem.journal.eissn | 1097-0258 | - |
Appears in Collections: | Research publications |
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