Question 7

College answer

Not available.

Discussion

a)

• Sensitivity = true positives / (true positives + false negatives)
• This is the proportion of disease which was correctly identified

b)

• Specificity = true negatives / (true negatives + false positives)
• This is the proportion of healthy patients in who disease was correctly excluded

c)

• Receiver Operating Characteristic (ROC) curve is a plot of sensitivity vs. false positive rate, for a number of test results.
• Sensitivity is on the y-axis, from 0% to 100%
• The ROC curve graphically represents the compromise between sensitivity and specificity in tests which produce results on a numerical scale, rather than binary (positive vs. negative results)
• The ROC curve determines the cut off point at which the sensitivity and specificity are optimal.
• AUC is the Area Under the ROC curve.
• The higher the AUC, the more accurate the test
• An AUC of 1.0 means the test is 100% accurate
• An AUC of 0.5 (50%) means the ROC curve is a a straight diagonal line, which represents the "ideal bad test", one which is only ever accurate by pure chance.
• When comparing two tests, the more accurate test is the one with an ROC curve further to the top left corner of the graph, with a higher AUC.
• The best cutoff point for a test (which separates positive from negative values) is the point on the ROC curve which is closest to the top left corner of the graph.