Key concepts: Receiver-operator characteristic (ROC) curves

When evaluating a diagnostic test, it is often difficult to determine the threshold laboratory value that separates a clinical diagnosis of “normal” from one of “abnormal.”

If you set a high threshold value (with the assumption that the test value increases with disease severity), you may miss some individuals with low test values or mild forms of the disease. The sensitivity, the fraction of people who have the disease that will be correctly identified with a positive test, will be low. Few of the positive tests will be false positives, but many of the negative tests will be false negatives.

On the other hand, if you set a low threshold, you will catch most individuals with the disease, but you may mistakenly diagnose many normal individuals as “abnormal.” The specificity, the fraction of people who don't have the disease who are correctly identified with a negative test, will be low. Few of the negative tests will be false negatives, but many of the positive tests will be false positives.

You can have higher sensitivity or higher specificity, but not both (unless you develop a better diagnostic test).

A receiver-operator characteristic (ROC) curve helps you visualize and understand the tradeoff between high sensitivity and high specificity when discriminating between clinically normal and clinically abnormal laboratory values.