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|Title:||Estimation of Optimally Combined-Biomarker Accuracy in the Absence of a Gold Standard Reference Test||Authors:||GARCIA BARRADO, Leandro
|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||Document URI:||http://hdl.handle.net/1942/16164||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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