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Updated: Jul 26, 2025
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
The relevance of competing risk adjustment in cardiovascular risk prediction models for clinical practice
Steven H J Hageman1, Jannick A N Dorresteijn1, Lisa Pennells2,3
1Department of Vascular Medicine, University Medical Centre Utrecht, Heidelberglaan 100, Postbus 85500 3508 GA Utrecht, The Netherlands.
Insights
Competing risk adjustment is crucial for accurate cardiovascular disease (CVD) risk prediction in high-risk groups. Models without this adjustment overestimate risk, potentially leading to overtreatment and impacting clinical decisions.
Area of Science:
- Cardiology
- Biostatistics
- Epidemiology
Background:
- Cardiovascular disease (CVD) risk prediction models often account for competing non-CVD mortality risks.
- This adjustment is believed to prevent overestimation of cumulative CVD incidence, particularly in high-risk populations.
- The clinical impact of competing risk adjustment in CVD prediction models for high-risk individuals requires evaluation.
Purpose of the Study:
- To evaluate the clinical impact of competing risk adjustment in cardiovascular disease (CVD) prediction models.
- To compare CVD risk predictions derived with and without competing risk adjustment in a high-risk population.
- To illustrate the effect of competing risk adjustment on treatment eligibility decisions.
Main Methods:
- Utilized data from the Utrecht Cardiovascular Cohort-Secondary Manifestations of ARTerial disease (UCC-SMART) study.
- Derived two 10-year residual CVD risk prediction models in 8355 individuals: one using Fine and Gray (competing risk) and one using Cox proportional hazards (non-competing risk).
- Compared model predictions, overestimation ratios, discrimination, and treatment eligibility based on risk thresholds.
Main Results:
- Models without competing risk adjustment yielded higher average predictions.
- The Cox model (without adjustment) overestimated cumulative CVD incidence (predicted-observed ratio 1.14), most notably in higher-risk quartiles and older individuals.
- Discrimination was similar between models, but the unadjusted Cox model led to more individuals being considered for treatment (44% vs. 34% at >20% risk threshold).
Conclusions:
- Individual risk predictions are higher when competing risks are not adjusted for.
- Competing risk adjustment is essential for accurate absolute risk prediction, especially in high-risk populations.
- Failure to adjust for competing risks can lead to overestimation and influence clinical decision-making regarding treatment eligibility.
Background:
Many models developed for predicting the risk of cardiovascular disease (CVD) are adjusted for the competing risk of non-CVD mortality, which has been suggested to reduce potential overestimation of cumulative incidence in populations where the risk of competing events is high. The objective was to evaluate and illustrate the clinical impact of competing risk adjustment when deriving a CVD prediction model in a high-risk population.
Methods And Results:
Individuals with established atherosclerotic CVD were included from the Utrecht Cardiovascular Cohort-Secondary Manifestations of ARTerial disease (UCC-SMART). In 8355 individuals, followed for a median of 8.2 years (IQR 4.2-12.5), two similar prediction models for the estimation of 10-year residual CVD risk were derived: with competing risk adjustment using a Fine and Gray model and without competing risk adjustment using a Cox proportional hazards model. On average, predictions were higher from the Cox model. The Cox model predictions overestimated the cumulative incidence [predicted-observed ratio 1.14 (95% CI 1.09-1.20)], which was most apparent in the highest risk quartiles and in older persons. Discrimination of both models was similar. When determining treatment eligibility on thresholds of predicted risks, more individuals would be treated based on the Cox model predictions. If, for example, individuals with a predicted risk > 20% were considered eligible for treatment, 34% of the population would be treated according to the Fine and Gray model predictions and 44% according to the Cox model predictions.
Interpretation:
Individual predictions from the model unadjusted for competing risks were higher, reflecting the different interpretations of both models. For models aiming to accurately predict absolute risks, especially in high-risk populations, competing risk adjustment must be considered.
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