ROC statistics are not without their disadvantages, however. They are not dependent on the prevalence of the disease, with the result that even a high AUC predictor of a very rare disease is often of little practical use. For instance, consider a predictor of a disease with a prevalence of 1%: even with specificity and sensitivity of 0.93 (typical of a test with an AUC of around 0.98) only 12% of the individuals who test positive will go on to develop the disease. Alternate statistics such as the positive and negative predictive values account for prevalence. The positive predictive value (PPV) is the proportion of people who test positive for the disease who go on to develop it, and the negative predictive value (NPV) is the proportion of people who test negative who remain healthy. Note that, like sensitivity and specificity, the positive and negative predictive values are dependent on the risk score threshold T. These statistics can be used to tune the parameter T: for instance, while in the example above a test of a 1% disease with an
