What's under the ROC? An introduction to receiver operating characteristics curves.
It is often necessary to dichotomize a continuous scale to separate respondents into normal and abnormal groups. However, because the distributions of the scores in these 2 groups most often overlap, any cut point that is chosen will result in 2 types of errors: false negatives (that is, abnormal cases judged to be normal) and false positives (that is, normal cases placed in the abnormal group). Changing the cut point will alter the numbers of erroneous judgments but will not eliminate the problem. A technique called receiver operating characteristic (ROC) curves allows us to determine the ability ofa test to discriminate between groups, to choose the optimal cut point, and to compare the performance of 2 or more tests. We discuss how to calculate and compareROC curves and the factors that must be considered in choosing an optimal cut point.
Streiner
David L
DL
Kunin-Lunenfeld Applied Research Unit, Baycrest Centre, Toronto, Ontario. dstreiner@klaru-baycrest.on.ca
Cairney
John
J
D006801:Humans; D001523:Mental Disorders / Q000175:diagnosis / Q000453:epidemiology; D011584:Psychology / Q000379:methods* / Q000706:statistics & numerical data*; D012372:ROC Curve; D012680:Sensitivity and Specificity