An ROC curve for two diagnostic tests (Test A and Test B) for detecting diabetes is plotted. Test A has an AUC of 0.92 and Test B has an AUC of 0.75. At the optimal cut-off, Test A shows sensitivity 88% / specificity 84%, while Test B shows 70% / 80%. Which statement is MOST accurate?

• A Test B is preferable for screening as it has higher specificity
• B Both tests are equally useful because AUC difference < 0.25 is clinically insignificant
• C Test A is superior overall; for diabetes screening, high sensitivity is preferred to minimise false negatives ✓
• D The optimal cut-off should always be set where sensitivity = specificity

Explanation

AUC (Area Under ROC Curve) represents overall discriminative ability: > 0.9 = excellent, 0.8–0.9 = good, 0.7–0.8 = fair. Test A (AUC 0.92) is superior to Test B (0.75). For a screening test, high sensitivity is prioritised to minimise false negatives (missing true cases), because missed diagnoses carry greater harm. The optimal cut-off is NOT necessarily where sensitivity = specificity — it depends on the clinical context and relative costs of false positives vs false negatives. For confirmatory tests, high specificity is preferred. The optimal point on the ROC curve minimises (1−sensitivity)² + (1−specificity)² or is selected by the Youden index (J = sensitivity + specificity − 1).

Reference: Park's Textbook of Preventive and Social Medicine, 27th ed.

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