Comparison of Competing Risks Models Based on Cumulative Incidence Function in Analyzing Time to Cardiovascular Diseases



Dianatkhah M^{1, 2} ; Rahgozar M¹ ; Talaei M^{2, 3} ; Karimloua M¹ ; Sadeghi M⁴ ; Oveisgharan S⁵ ; Sarrafzadegan N² Show Affiliations
Source: ARYA Atherosclerosis Published:2014

Abstract

BACKGROUND: Competing risks arise when the subject is exposed to more than one cause of failure. Data consists of the time that the subject failed and an indicator of which risk caused the subject to fail. METHODS: With three approaches consisting of Fine and Gray, binomial, and pseudo-value, all of which are directly based on cumulative incidence function, cardiovascular disease data of the Isfahan Cohort Study were analyzed. Validity of proportionality assumption for these approaches is the basis for selecting appropriate models. Such as for the Fine and Gray model, establishing proportionality assumption is necessary. In the binomial approach, a parametric, non-parametric, or semi-parametric model was offered according to validity of assumption. However, pseudo-value approaches do not need to establish proportionality. RESULTS: Following fitting the models to data, slight differences in parameters and variances estimates were seen among models. This showed that semi-parametric multiplicative model and the two models based on pseudo-value approach could be used for fitting this kind of data. CONCLUSION: We would recommend considering the use of competing risk models instead of normal survival methods when subjects are exposed to more than one cause of failure.