The ROC curve is used to evaluate how well a diagnostic test can distinguish between two groups (eg, diseased vs. healthy). It is a plot of the true positive rate (sensitivity) on the y-axis against the false positive rate (1 – specificity) on the x-axis.
A test with better discriminative ability will have a curve that bows closer to the upper left corner of the graph and a larger area under the curve (AUC).
Learning Objective: Understand how to interpret ROC curves and the AUC to assess the diagnostic accuracy of a test.

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