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B0303
Title: Robust estimation of ROC curves with covariates Authors:  Ana Maria Bianco - Universidad de Santiago de Compostela (Spain) [presenting]
Graciela Boente - Universidad de Buenos Aires (Argentina)
Wenceslao Gonzalez-Manteiga - University of Santiago de Compostela (Spain)
Abstract: Receiver Operating Characteristic (ROC) curves are a useful graphical tool to measure the discriminating power of a continuous variable, such as diagnostic variable or a marker. They are employed to quantify the accuracy of the marker to distinguish between two conditions or classes. As with any classifier, the assignations are not perfect and may lead to classification errors. In practical situations, the discriminatory effectiveness of the marker under study may be affected by several factors. When for each individual additional information is available, it is sensible to include it in the ROC analysis. The aim is to show the instability of the conditional ROC curve in presence of outliers and also to provide robust estimators when covariates are available. A semiparametric approach is followed, where robust parametric estimators are combined with weighted empirical distribution estimators based on an adaptive procedure that downweights outliers. The consistency of the proposal is discussed. Through a Monte Carlo study, the performance of the proposed estimators is compared with that of the classical ones in clean and contaminated samples.