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Title: Estimation of Optimally Combined-Biomarker Accuracy in the Absence of a Gold Standard Reference Test
Authors: GARCIA BARRADO, Leandro 
COART, Elisabeth
Issue Date: 2013
Publisher: Springer International Publishing
Source: Lanzarone, Ettore; Ieva, Francesca (Ed.). The Contribution of Young Researchers to Bayesian Statistics: Proceedings of BAYSM2013, p. 7-10
Series/Report: Springer Proceedings in Mathematics & Statistics
Series/Report no.: 63
Abstract: The reference diagnostic test used to establish the discriminative properties of a combination of biomarkers could be imperfect. This may lead to a biased estimate of the accuracy of the combination. A Bayesian latent-class mixture model is proposed to estimate the Area Under the ROC Curve (AUC) of a combination of biomarkers. The model allows selecting the combination that maximizes the AUC and takes possible errors in the reference test into account. A simulation study was performed based on 400 data sets. Sample sizes from 100 to 600 observations were considered. Informative as well as non-informative prior information for the diagnostic accuracy of the reference test was considered. In addition, a controlled prior specification is proposed. The obtained average estimates for all parameters were close to the true values; some differences in efficiency were observed. Results indicate an adequate performance of the model-based estimates.
Keywords: Bayesian estimation; latent class mixture models; AUC
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ISBN: 978-3-319-02084-6
DOI: 10.1007/978-3-319-02084-6
Category: C1
Type: Proceedings Paper
Appears in Collections:Research publications

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