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Related Experiment Video

Updated: Jun 24, 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

Prognostic models with competing risks: methods and application to coronary risk prediction.

Marcel Wolbers¹, Michael T Koller, Jacqueline C M Witteman

¹Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel, Basel, Switzerland. mwolbers@oucru.org

Epidemiology (Cambridge, Mass.)

|April 16, 2009

Summary

In frail populations, competing risks are crucial for accurate disease prediction. Ignoring these risks, like non-coronary heart disease death, leads to overestimated absolute risk, impacting clinical decisions.

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Last Updated: Jun 24, 2026

An R-Based Landscape Validation of a Competing Risk Model

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Published on: September 16, 2022

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

Biostatistics
Epidemiology
Gerontology

Background:

Clinical decision-making often depends on absolute disease risk.
Frail populations face competing risks that can prevent the event of interest.

Purpose of the Study:

To review competing-risk regression models for predictive modeling.
To adapt prognostic performance measures for competing risks.
To compare models for coronary heart disease (CHD) prediction in elderly women.

Main Methods:

Review of competing-risk regression models.
Adaptation of calibration and discrimination measures.
Comparison of Fine and Gray model, standard Cox model, and cause-specific hazards model using Rotterdam Study data.

Main Results:

The Fine and Gray and cause-specific hazards models performed similarly.
The standard Cox model significantly overestimated 10-year CHD risk.
High-risk classification differed: 18% (Cox) vs. 8% (Fine and Gray).

Conclusions:

Competing risks must be explicitly considered in frail populations, such as the elderly.
Standard Cox models are inadequate when competing risks are present.
Accurate risk prediction in elderly requires accounting for competing events.