Cox Regression Models with Time-Varying Covariates Applied to Survival Success of Young Firms

The most widely used model in multivariate analysis of survival data is proportional hazards model proposed by ox. While it is easy to get and interpret the results of the model, the basic assumption of proportional hazards model is that independent variables assumed to remain constant throughout the observation period. Model can give biased results in cases which this assumption is violated. ne of the methods used modelling the hazard ratio in the cases that the proportional hazard assumption is not met is to add a time-dependent variable showing the interaction between the predictor variable and a parametric function of time. In this study, we investigate the factors that affect the survival time of the firms and the time dependence of these factors using ox regression considering time-varying variables. The firm data comes from Business Development enters ( ISGEM) which is a prominent business incubation center operating in Turkey.