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Updated: Feb 25, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
[Application of Competing Risks Model in Predicting Smoking Relapse Following Ischemic Stroke]
Li-Sha Hou1, Ji-Jie Li1, Xu-Dong Du1
1Department of Epidemiology and Health Statistics, West China School of Public Health, Sichuan University, Chengdu 610041, China.
Objective:
To determine factors associated with smoking relapse in men who survived from their first stroke.
Methods:
Data were collected through face to face interviews with stroke patients in the hospital, and then repeated every three months via telephone over the period from 2010 to 2014. Kaplan-Meier method and competing risk model were adopted to estimate and predict smoking relapse rates.
Results:
The Kaplan-Meier method estimated a higher relapse rate than the competing risk model. The four-year relapse rate was 43.1% after adjustment of competing risk. Exposure to environmental tobacco smoking outside of home and workplace (such as bars and restaurants) (P=0.01), single (P<0.01), and prior history of smoking at least 20 cigarettes per day (P=0.02) were significant predictors of smoking relapse.
Conclusion:
When competing risks exist, competing risks model should be used in data analyses. Smoking interventions should give priorities to those without a spouse and those with a heavy smoking history. Smoking ban in public settings can reduce smoking relapse in stroke patients.
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