The simplest measures of classification accuracy are the sensitivity and specificity of the test, respectively defined as the proportion of individuals who develop the disease who were classified as high risk, and the proportion of healthy individuals classified as low risk. These values vary with the choice of T, which represents the unavoidable trade-off between sensitivity and specificity: predicting everyone will become ill will guarantee complete sensitivity, but without any specificity. A plot of the sensitivity against 1-specificity for all possible choices of T is known as a receiver-operating characteristic (ROC) curve (9). The area under the ROC curve (the AUC, sometimes called the C statistic) has the pleasing property of being equal to the probability that a randomly selected individual with the disease has a higher score than a randomly selected healthy individual.
