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Title: Robust inference for ROC regression Authors:  Vanda Lourenco - Faculty of Sciences and Technology - New University of Lisbon (Portugal) [presenting]
Vanda Inacio - University of Edinburgh (United Kingdom)
Miguel de Carvalho - School of Mathematics, University of Edinburgh (Portugal)
Abstract: The receiver operating characteristic (ROC) curve is the most popular tool for evaluating the diagnostic accuracy of continuous biomarkers. Often, covariate information that affects the biomarker performance is also available and several regression methods have been proposed to incorporate covariates in the ROC framework. We propose robust inference methods for ROC regression, which can be used to safeguard against the presence of outlying biomarker values. Simulation results suggest that the methods perform well in recovering the true conditional ROC curve and corresponding area under the curve, on a variety of data contamination scenarios. Methods are illustrated using data on age-specific accuracy of glucose as a biomarker of diabetes.