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. Author manuscript; available in PMC: 2009 Nov 1.
Published in final edited form as: Pharmacogenet Genomics. 2008 Nov;18(11):955–965. doi: 10.1097/FPC.0b013e32830efdd4

Figure 4.

Figure 4

ROC curves from various models showing improvement of discrimination ability. True positives (sensitivity) were plotted on the y-axis and false positives (1-specificity) on the x-axis. The area under curve (AUC) measures the discrimination ability of the ROC test to correctly classify those with and without the outcome (disease, response, death, etc). An AUC of 1 represents a perfect discrimination (100% sensitivity and specificity); an AUC of 0.5 represents an accuracy that would be achieved by chance alone (equivalent to tossing a coin); an AUC of 0.83 indicates that there is a 83% likelihood that the model will correctly classify two randomly drawn pairs of patients into dead and survival groups based on the variables included in the model.