biomarker-roc

name	biomarker-roc
description	Evaluate biomarker performance with ROC analysis, AUC, sensitivity, and specificity.

Biomarker ROC Analysis

Evaluate biomarker diagnostic or prognostic performance.

Metrics

• AUC: Area under ROC curve
• Sensitivity: True positive rate
• Specificity: True negative rate
• PPV: Positive predictive value
• NPV: Negative predictive value
• Optimal cutoff: Youden index or clinical threshold

Workflow

1. Extract study data (contingency table or raw values)
2. Calculate sensitivity/specificity at various cutoffs
3. Compute AUC with 95% CI (DeLong method)
4. Compare with existing biomarkers
5. Assess clinical utility (does it change management?)

Output

# Biomarker ROC: [biomarker] in [disease]

## Study Data
| Study | N | Cases | Controls | AUC (95% CI) | Cutoff | Sen | Spe |

## Meta-Analysis (if ≥3 studies)
- Pooled AUC
- Heterogeneity

## Clinical Utility
- Incremental value over standard markers
- Cost-effectiveness (if data available)

## Validation Gaps

Save to outputs/biomarker-roc-<slug>.md.