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Updated: Aug 6, 2026
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Application of competing risks models in cardiovascular mortality research: findings from the Tehran lipid and
Masoumeh Mahdi-Akhgar1, Ebrahim Hajizadeh1, Ahmadreza Baghestani2
1Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
Purpose:
Cardiovascular diseases (CVD) are a leading cause of mortality in Iran and globally. This study aimed to provide more accurate estimates of associations between risk factors and CVD mortality by applying competing risk models within the Tehran Lipid and Glucose Study cohort.
Methods:
In this prospective analysis, 7,529 individuals aged ≥ 30 years without prevalent CVD were followed for a median of 19.87 years. The primary outcome was CVD mortality (n = 311), with non-CVD death as the competing event (n = 592). Analyses were stratified by sex and age (< 65 vs. ≥65 years). Cause-specific and Fine-Gray models estimated hazard ratios for diabetes, hypertension, hypercholesterolemia, smoking, and body mass index.
Results:
Diabetes and hypertension were the strongest predictors of CVD mortality across most subgroups. In the Fine-Gray model, diabetes showed the greatest impact in women < 65 years (HR: 4.83, p < 0.001), while hypertension showed the strongest association in women ≥ 65 years (HR: 3.32, p < 0.001). Hypercholesterolemia was associated with increased CVD mortality exclusively in women < 65 years (HR: 1.79, p = 0.02). Body mass index showed no significant association.
Conclusion:
Diabetes and hypertension are the predominant risk factors for CVD mortality in the presence of competing risks, with effect magnitudes varying by sex and age. Applying competing risk models is essential for accurate risk estimation and targeted prevention in populations with high competing mortality burden.
