ROC/AUC Calculator

ROC Analysis Fundamentals

ROC analysis evaluates the discrimination ability of a binary classifier or diagnostic test with a continuous output. Key concepts:

• Threshold: The cutoff value above which a case is classified as positive • Sensitivity (TPR): Proportion of true positives correctly identified • Specificity (1 − FPR): Proportion of true negatives correctly identified • AUC: Summary measure of overall discrimination (0.5 = chance, 1.0 = perfect) • Youden index: Optimal threshold maximizing J = Sens + Spec − 1

The ROC curve is threshold-invariant — it shows performance across all possible thresholds, making it more informative than accuracy at a single cutoff.

Interpreting the Threshold Table

The threshold table shows diagnostic accuracy metrics at each unique score value. Use it to:

• Find the Youden-optimal threshold (highlighted) — maximizes combined sensitivity and specificity • Choose a sensitivity-first threshold — for screening, find the lowest threshold that gives $\geq$ 90% sensitivity • Choose a specificity-first threshold — for confirmation, find the highest threshold that gives $\geq$ 95% specificity • Evaluate PPV/NPV — at your chosen threshold, considering the prevalence in your target population