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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/21510

Title: Development of a diagnostic test based on multiple continuous biomarkers with an imperfect reference test
Authors: Garcia Barrado, Leandro
Coart, Els
Burzykowski, Tomasz
Issue Date: 2016
Citation: STATISTICS IN MEDICINE, 35 (4), p. 595-608
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.
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.
URI: http://hdl.handle.net/1942/21510
DOI: 10.1002/sim.6733
ISI #: 000368944800012
ISSN: 0277-6715
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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