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Title: Smooth time-dependent ROC curve estimators Authors:  Juan-Carlos Pardo-Fernandez - Universidade de Vigo (Spain) [presenting]
Pablo Martinez-Camblor - Geisel School of Medicine Dartmouth College (United States)
Abstract: The ROC curve is a popular graphical tool often used to study the diagnostic capacity of continuous (bio)markers. When the considered outcome is a time-dependent variable, two main extensions have been proposed: the cumulative/dynamic ROC curve and the incident/dynamic ROC curve. In both cases, the main problem for developing appropriate estimators is the estimation of the joint distribution of the variables time-to-event and marker. As usual, different approximations lead to different estimators. We explore the use of a bivariate kernel density estimator which accounts for censored observations and produces smooth estimators of the time-dependent ROC curves. The performance of the resulting cumulative/dynamic and incident/dynamic ROC curves is studied by means of Monte Carlo simulations. Additionally, the influence of the choice of the required smoothing parameters is explored. The proposed methodology is illustrated with applications to real data.