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Models of Health Promotion and Illness Prevention I

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Related Experiment Video

Updated: Jun 11, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Evaluating health risk models.

Alice S Whittemore¹

¹Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA 94305-5405, USA. alicesw@stanford.edu

Statistics in Medicine

|July 13, 2010

Summary

This summary is machine-generated.

Evaluating disease risk models requires understanding performance metrics and how adding covariates impacts accuracy. New methods identify subgroups benefiting most from additional data for targeted, cost-effective risk assessment.

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Area of Science:

Biostatistics
Epidemiology
Health Services Research

Background:

Targeted disease prevention necessitates accurate individual risk prediction models.
Evaluating and comparing risk models, especially when expanding covariates, is crucial.

Purpose of the Study:

To review and relate various performance measures for risk models.
To demonstrate how model performance criteria depend on application context.
To propose methods for identifying subgroups with significant gains from additional covariates.

Main Methods:

Review of statistical performance measures for risk prediction models.
Application of measures to hypothetical populations and US breast cancer risk models.
Development of novel approaches to quantify subgroup-specific gains from added covariates.

Main Results:

Model performance is influenced by covariate distributions within a population.
Summary performance measures can obscure important subgroup-specific utility.
Significant gains from additional covariates can be concentrated in specific subgroups.

Conclusions:

Appropriate risk model evaluation depends on the intended use and population.
Identifying subgroups with the largest gains enables cost-efficient covariate assessment.
Targeted covariate measurement can optimize resource allocation for disease prevention.