ROC curves and the binormal assumption.
Previous articles in this series have described how receiver operating characteristic (ROC) graphs provide comprehensive graphic representations of the diagnostic performance of non-binary tests and have explained how one constructs "trapezoidal" ROC graphs in which discrete cutoff points are plotted and connected with line segments. In this article, we describe a set of mathematical assumptions that permit the generation of a continuous, smooth ROC curve for a given diagnostic test. These assumptions permit us to characterize a test's performance using a small number of parameters and also to explore properties of diagnostic tests. In this article, we describe a set of mathematical assumptions that can be used to link receiver operating characteristic (ROC) curves to the underlying distribution of values of the diagnostic variable being measured. We will illustrate these assumptions using a diagnostic test that distinguishes alcohol abusers from normal consumers of alcohol and abstainers.
Somoza
E
E
Psychiatry Service, Department of Veterans Affairs Medical Center, Cincinnati, OH 45220.
Mossman
D
D
D000437:Alcoholism / Q000097:blood / Q000175:diagnosis*; D006801:Humans; D008111:Liver Function Tests / Q000706:statistics & numerical data*; D016011:Normal Distribution; D011237:Predictive Value of Tests; D012372:ROC Curve; D014168:Transferrin / Q000031:analogs & derivatives* / Q000378:metabolism
D014168:Transferrin; C056759:carbohydrate-deficient transferrin