Suppose you have an x value that is a diagnostic measurement and you want to determine a threshold value of x that indicates the following:
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A condition exists if the x value is greater than the threshold.

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A condition does not exist if the x value is less than the threshold.

For example, you could measure a blood component level as a diagnostic test to predict a type of cancer. Now consider the diagnostic test as you vary the threshold and, thus, cause more or fewer false positives and false negatives. You then plot those rates. The ideal is to have a very narrow range of x criterion values that best divides true negatives and true positives. The Receiver Operating Characteristic (ROC) curve shows how rapidly this transition happens, with the goal being to have diagnostics that maximize the area under the curve.
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Sensitivity, the probability that a given x value (a test or measure) correctly predicts an existing condition. For a given x, the probability of incorrectly predicting the existence of a condition is 1 – sensitivity.

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Specificity, the probability that a test correctly predicts that a condition does not exist.

A ROC curve is a plot of sensitivity by (1 – specificity) for each value of x. The area under the ROC curve is a common index used to summarize the information contained in the curve.
When you do a simple logistic regression with a binary outcome, there is a platform option to request a ROC curve for that analysis. After selecting the ROC Curve option, a window asks you to specify which level to use as positive.