PPV NPV

PPV NPV integrates positive predictive value (PPV) and negative predictive value (NPV) criteria into receiver operating characteristic (ROC) analysis to evaluate prognostic tests while explicitly accounting for disease prevalence.

Key Features:

• Equi-PPV and Equi-NPV Equations: Provides explicit equations for plotting equi-PPV and equi-NPV lines within ROC space.
• Prevalence-aware Evaluation: Incorporates disease prevalence into the assessment of test performance rather than relying solely on AUROC.
• Sensitivity–Specificity Pair Plotting: Plots sensitivity–specificity pairs that satisfy specified PPV or NPV criteria for given prevalences.
• Co-visualization with ROC/AUROC Metrics: Enables visualization of predictive-value criteria alongside traditional ROC curves and AUROC values.
• ROC Shape Identification: Identifies ROC curve shapes that best meet target PPV and NPV thresholds.
• Rule-in versus Rule-out Design: Distinguishes requirements for tests intended for high PPV (rule-in) versus high NPV (rule-out) use cases.
• Case Study Application: Demonstrates application to predicting pre-eclampsia risk in low-risk nulliparous pregnancies.

Scientific Applications:

• Prognostic Test Evaluation: Assess prognostic model performance with PPV/NPV criteria integrated into ROC analysis.
• Clinical Translation: Inform selection and development of tests that meet clinical prevalence-dependent utility criteria.
• Risk Stratification: Apply prevalence-aware predictive-value assessment for clinical conditions such as pre-eclampsia in low-risk nulliparous pregnancies.
• Test Design Optimization: Guide design of distinct rule-in (high PPV) and rule-out (high NPV) diagnostic or prognostic tests.
• Beyond AUROC Assessment: Provide complementary evaluation to AUROC by quantifying PPV and NPV implications at specified prevalences.

Methodology:

Derives equations for equi-PPV and equi-NPV lines in ROC space and plots sensitivity–specificity pairs that satisfy PPV/NPV criteria across specified disease prevalences, enabling co-visualization with ROC metrics and identification of ROC curve shapes that meet PPV/NPV thresholds.