Overview of Receiver Operating Characteristic (ROC) Curves in SAS

The repeated dosing of some drugs can induce injury to the human liver. Regular monitoring of biomarkers assayed in blood samples may help to diagnose safety issues sooner. There is interest in developing new biomarkers that are more specific than the standard tests [e.g. Alanine Transaminase (ALT)] commonly used. 

In medical diagnostics, a receiver operating characteristic (ROC) analysis is a powerful statistical analysis tool that is used to assess the ability of a test to correctly classify diseased (sensitivity) and non-diseased (specificity) subjects. The sensitivity and specificity rates are used to construct the ROC curve which is used to visually inspect the ability of the test to discriminate between patients’ true status of disease. The most widely used summary statistic is the area under the ROC curve (AUROC).

We present recent enhancements to PROC LOGISTIC for constructing ROC curves and compare AUROCs between biomarkers with standard errors and 95% confidence intervals (CIs). An overview of the code, output and interpretation of the ROC features of PROC LOGISTIC in SAS v9.3 using simulated data on two candidate biomarkers is examined; and we discuss the limitations of ROC analysis in the context of identifying and validating the best candidate biomarker in the infographic below.