What is a ROC curve and what does AUC measure?

• A. ROC plots true positive rate vs false positive rate; AUC measures overall diagnostic accuracy (0.5–1.0)
• B. ROC plots false negative rate vs true negative rate
• C. AUC is the p-value of the test
• D. ROC curve is not used in diagnostic tests

Answer: (A)

The main idea is that a ROC curve shows how a diagnostic test performs across all possible thresholds by plotting sensitivity (true positive rate) against 1 minus specificity (false positive rate). As you change the threshold, you trade off catching more true positives against producing more false positives, and the curve traces that trade-off.

The area under that curve, the AUC, is a single summary of the test’s overall ability to discriminate between positive and negative cases. An AUC of 0.5 means the test does no better than random guessing, while an AUC of 1.0 indicates perfect discrimination. Values between 0.5 and 1.0 reflect varying levels of diagnostic accuracy. AUC captures the test’s performance regardless of a specific threshold, which is why it’s a common measure of overall diagnostic ability.