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Updated: Apr 16, 2026
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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
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[Dealing with competing events in survival analysis].
Clémence Béchade1, Thierry Lobbedez1,
1Service de néphrologie, CHU Clémenceau, avenue G.-Clémenceau, 14033 Caen cedex, France.
Nephrologie & Therapeutique
|March 15, 2015
Summary
Survival analysis can be biased by competing events. The Cox model is inappropriate for competing risks, but the Fine and Gray model offers a solution for accurate risk estimation.
Area of Science:
- Biostatistics
- Epidemiology
- Medical Statistics
Background:
- Survival analysis is crucial for determining risk factors and estimating event risk.
- Competing events can hinder the observation of the primary event of interest.
- Ignoring competing events can introduce significant bias into risk estimations.
Purpose of the Study:
- To elucidate the limitations of the Cox model in the presence of competing events.
- To introduce and advocate for the use of the Fine and Gray model for competing risks analysis.
Main Methods:
- Discussion of the theoretical underpinnings of survival analysis.
- Comparative analysis of the Cox model and the Fine and Gray model.
- Explanation of how the Fine and Gray model addresses bias from competing risks.
Main Results:
- The standard Cox model yields biased risk estimates when competing events are present.
- The Fine and Gray model provides adjusted hazard ratios that account for competing risks.
- Demonstration of improved accuracy in risk prediction using the Fine and Gray model.
Conclusions:
- The Cox model is not suitable for survival data with competing events.
- The Fine and Gray model is a statistically sound approach for analyzing competing risks.
- Utilizing the Fine and Gray model is recommended for unbiased risk factor identification and estimation in the presence of competing events.
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