The receiver operating characteristic (ROC) curve is a popular method for characterizing the accuracy of diagnostic tests when test results are not binary. Various methodologies for estimating and comparing ROC curves have been developed. One approach, due to Pepe, uses a parametric regression model ROC x u = g h 0 u + 0 x with the baseline function h 0 u specified up to a finite-dimensional parameter. In this article we extend the regression models by allowing arbitrary nonparametric baseline

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... unctions. We also provide asymptotic distribution theory and procedures for making statistical inference. We illustrate our approach with dataset from a prostate cancer biomarker study. Simulation studies suggest that the extra flexibility inherent in the semiparametric method is gained with little loss in statistical efficiency. A perfect biomarker is one for which at some threshold c * , S D c * = 1 and S D c * = 0. More usually, there is a tradeoff between S D and S D displayed through the ROC curve, a plot of the true-positive rates versus the false-positive rates,