Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine

TLDR

Diagnostic accuracy measures a laboratory test’s ability to correctly classify subjects into clinically relevant subgroups, and ROC plots are a central tool for evaluating this accuracy across all operating conditions. ROC plots generate a pure accuracy index by illustrating a test’s discriminative limits across all thresholds, enabling subsequent quantitative analyses, threshold selection, likelihood ratio updates, and integration into clinical decision strategies.

Abstract

The clinical performance of a laboratory test can be described in terms of diagnostic accuracy, or the ability to correctly classify subjects into clinically relevant subgroups. Diagnostic accuracy refers to the quality of the information provided by the classification device and should be distinguished from the usefulness, or actual practical value, of the information. Receiver-operating characteristic (ROC) plots provide a pure index of accuracy by demonstrating the limits of a test's ability to discriminate between alternative states of health over the complete spectrum of operating conditions. Furthermore, ROC plots occupy a central or unifying position in the process of assessing and using diagnostic tools. Once the plot is generated, a user can readily go on to many other activities such as performing quantitative ROC analysis and comparisons of tests, using likelihood ratio to revise the probability of disease in individual subjects, selecting decision thresholds, using logistic-regression analysis, using discriminant-function analysis, or incorporating the tool into a clinical strategy by using decision analysis.