Development of a diagnostic test based on multiple continuous biomarkers with an imperfect reference test.

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 s

SUBMITTER: Garcia Barrado L 

PROVIDER: S-EPMC6312185 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature