Author/Authors :
Dianatkhah، Minoo نويسنده Department of Statistics and Computer Sciences, University of Social Welfare and Rehabilitation Sciences, Tehran AND Isfahan Cardiovascular Research Center, Isfahan Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran , , Rahgozar، Mehdi نويسنده Department of Statistical, Faculty of Statistical and Computer, Welfare & Rehabilitation Sciences University, Tehran, Iran Rahgozar, Mehdi , Talaei، Mohammad نويسنده Medical doctor, Isfahan Cardiovascular Research Center, Isfahan University of Medical Sciences, Isfahan, Iran , , Karimloua، Masoud نويسنده Assistant Professor, Department of Statistics and Computer Sciences, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran , , Sadeghi، Masoumeh نويسنده , , Oveisgharan، Shahram نويسنده Department of Neurology, Tehran University of Medical Sciences, Tehran, Iran , , Sarrafzadegan، Nizal نويسنده MD, Professor of Cardiology, Isfahan Cardiovascular Research Center, IUMS, Isfahan ,
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.
