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Updated: Jun 6, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Two criteria for evaluating risk prediction models.

R M Pfeiffer1, M H Gail

  • 1Biostatistics Branch, National Cancer Institute, 6120 Executive Boulevard, Bethesda, Maryland 20892, USA. pfeiffer@mail.nih.gov

Biometrics
|December 16, 2010
PubMed
Summary
This summary is machine-generated.

We introduce two criteria, PCF(q) and PNF(p), to evaluate risk prediction models for disease screening and prognosis. These metrics assess how effectively high-risk populations are identified and followed for intervention.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Medical Informatics

Background:

  • Accurate risk prediction models are crucial for effective disease screening, prevention, and patient management.
  • Existing methods for evaluating model utility may not fully capture the practical implications for public health interventions.

Purpose of the Study:

  • To propose and study two novel criteria, Proportion of Cases Followed (PCF) and Proportion Needed to Follow-up (PNF), for assessing the usefulness of risk prediction models.
  • To establish the theoretical underpinnings and practical application of these criteria in evaluating screening and prognostic models.

Main Methods:

  • Defined PCF(q) as the proportion of future disease cases captured within the highest-risk q% of the population.
  • Defined PNF(p) as the proportion of the population that needs to be followed to identify p% of future cases.
  • Utilized Lorenz curves and their inverses to illustrate the relationship with PCF and PNF.
  • Developed distribution theory for PCF and PNF estimates.
  • Employed influence functions for statistical inference and model comparison.

Main Results:

  • Demonstrated the relationship between PCF, PNF, and Lorenz curves.
  • Provided theoretical distribution for estimates of PCF and PNF.
  • Developed novel inference methods for single risk models and for comparing multiple models using PCF and PNF.

Conclusions:

  • PCF and PNF offer valuable, interpretable metrics for assessing the utility of risk prediction models in clinical and public health settings.
  • The proposed methods facilitate robust statistical inference and comparison of risk models, aiding in the selection of optimal tools for disease management.