A comparison of parametric and nonparametric approaches to ROC analysis of quantitative diagnostic tests

Receiver operating characteristic (ROC) analysis, which yields indices of accuracy such as the area under the curve (AUC), is increasingly being used to evaluate the performances of diagnostic tests that produce results on continuous scales. Both par-ametric and nonparametric ROC approaches are available to assess the discriminant capacity of such tests, but there are no clearguidelines as to the merits of each, particularly with non-binormal data. Investigators may worry that when data ar non-Gaussian, estimates of diagnostic accuracy based on a binormal model may be dis-torted. The authors conducted a Monte Carlo simulation study to compare the bias and sampling variability in the estimates of the AUCs derived from parametric and nonparametric procedures. Each approach was assessed in data sets generated from various configurations of pairs of overlapping distributions; these included the binormal model and non-binormal pairs of distributions where one or both pair members were mixtures of Gaussian (MG) distributions with different degrees of departures from bi-normality. The biases in the estimates of the AUCs were found to be very small for both parametric and nonparametrlc procedures. The two approaches yielded ver