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Which is best sensitivity or specificity?
In general, high sensitivity tests have low specificity. In other words, they are good for catching actual cases of the disease but they also come with a fairly high rate of false positives. Mammograms are an example of a test that generally has a high sensitivity (about 70-80%) and low specificity.
Why is the sensitivity and specificity of a test important?
Sensitivity and Specificity Both are needed to fully understand a test’s strengths as well as its shortcomings. Sensitivity measures how often a test correctly generates a positive result for people who have the condition that’s being tested for (also known as the “true positive” rate).
The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings. Each point of the ROC curve (i.e. threshold) corresponds to specific values of sensitivity and specificity.
How does the AUC relate to the ROC curve?
Each point of the ROC curve (i.e. threshold) corresponds to specific values of sensitivity and specificity. The area under the ROC curve (AUC) is a summary measure of performance, that indicates whether on average a true positive is ranked higher than a false positives.
Why are ROC curves used in radar testing?
The initial research was motivated by the desire to determine how the US RADAR “receiver operators” had missed the Japanese aircraft. Now ROC curves are frequently used to show the connection between clinical sensitivity and specificity for every possible cut-off for a test or a combination of tests.
Which is the best cut off for a ROC curve?
The best cut-off has the highest true positive rate together with the lowest false positive rate. As the area under an ROC curve is a measure of the usefulness of a test in general, where a greater area means a more useful test, the areas under ROC curves are used to compare the usefulness of tests.