rock curve
A graphical plot evaluating diagnostic test performance across all possible classification thresholds.
The ROC curve reveals biomarker performance at a glance:
The closer the curve approaches the upper-left corner, the better the biomarker distinguishes between groups (e.g., responders vs non-responders).
Simple Interpretation: A curve hugging the top-left corner = good test. A curve near the diagonal = useless test. The area under the curve (AUC) puts a number on this.
ROC analysis can compare functional and expression biomarkers:
Example from PD-1/PD-L1Loading... studies:
This quantitative comparison demonstrates why functional measurement provides superior clinical value.
The Proof: When you compare ROC curves for FRET-based interaction measurement vs IHC expression, functional measurement has a bigger area under the curve—it's a better test.
