J. C. Pardo-Fernández, P. Martínez-Camblor
The ROC curve is routinely used for evaluating the performance of a continuous marker as diagnostic tool in a binary classification problem. In many practical applications, covariates related to the marker may be available. Under these circumstances, it is of interest to evaluate the influence that those covariates might have in the performance of the marker in terms of classification ability. Two extensions of the ROC are commonly used: the covariate-specific ROC curve and the covariate-adjusted ROC curve. In this talk we will review these concepts. Since they are strongly related with the conditional distribution of the marker, the use of proportional hazard regression models arises in a very natural way. We will explore the use of flexible proportional hazard Cox regression models for estimating the covariate-specific and the covariate-adjusted ROC curves. We study their large- and finite-sample properties and apply the proposed estimators to a real-world problem.
Keywords: ROC curve, covariate, semiparametric estimator
Scheduled
Statistical Models
June 10, 2025 5:10 PM
Auditorio 2. Leandre Cristòfol