What does AUC-ROC measure in diagnostic test evaluation?

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Multiple Choice

What does AUC-ROC measure in diagnostic test evaluation?

Explanation:
AUC-ROC measures how well a diagnostic test can distinguish between diseased and non-diseased individuals across all possible thresholds. The ROC curve shows sensitivity (true positive rate) versus 1 minus specificity (false positive rate) as you vary the decision threshold, and the area under that curve summarizes the test’s overall discriminative power in a single number. Think of it as the likelihood that a randomly chosen diseased person will have a higher test score than a randomly chosen non-diseased person. A perfect test reaches 1.0, meaning perfect separation at some threshold, while a test that performs no better than chance sits at 0.5. This measure focuses on ranking ability, not on calibrating predicted probabilities or on consistency across repeated measurements.

AUC-ROC measures how well a diagnostic test can distinguish between diseased and non-diseased individuals across all possible thresholds. The ROC curve shows sensitivity (true positive rate) versus 1 minus specificity (false positive rate) as you vary the decision threshold, and the area under that curve summarizes the test’s overall discriminative power in a single number.

Think of it as the likelihood that a randomly chosen diseased person will have a higher test score than a randomly chosen non-diseased person. A perfect test reaches 1.0, meaning perfect separation at some threshold, while a test that performs no better than chance sits at 0.5.

This measure focuses on ranking ability, not on calibrating predicted probabilities or on consistency across repeated measurements.

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