A very common situation in biostatistics, but also much more broadly of course, is that one wants to compare the predictive ability of two competing models. A key question of interest often is whether adding a new marker or variable Y to an existing set X improves prediction. The most obvious way of testing this hypothesis is to use a regression model, and then test whether adding the new variable Y improves fit, by testing the null hypothesis that the coefficient of Y in the expanded model differs from zero. An alternative approach is to test whether adding the new variable improves some measure of predictive ability, such as the area under the ROC curve.