The Estimation of Survival Function for Colon Cancer Data in Tehran Using Non-parametric Bayesian Model

Abstract Background: Colon cancer is the third cause of cancer deaths. Although colon cancer survival time has increased in recent years, the mortality rate is still high. The Cox model is the most common regression model often used in medical research in survival analysis, but most of the time the effect of at least one of the independent factors changes over time, so the model cannot be used. In the current study, the survival function for colon cancer patients in Tehran is estimated using non-parametric Bayesian model. Methods: In this survival study, 580 patients with colon cancer who were recorded in the Cancer Research Center of Shahid Beheshti University of Medical Sciences since April 2005 to November 2006 were studied and followed up for a period of 5 years. Survival function was plotted with non-parametric Bayesian model and was compared with the Kaplan–Meier curve. Results: Of the total of 580 patients, 69.9% of patients were alive. 45.9% of patients were male and the mean age of cancer diagnosis was 65.12 (SD= 12.26) and 87.7 of the patients underwent surgery. There was a significant relationship between age at diagnosis and sex and the survival time while there was a non-significant relationship between the type of treatment and the survival time. The survival functions corresponding to the two treatment groups cross, in comparison with the patients who had no surgery in the first 30 months, showed a higher level of risk in the patients who underwent a surgery. After that, the survival probability for the patients undergoing a surgery has increased. Conclusion: The study showed that survival rate has been higher in women and in the patients who were below 60 years at the time of diagnosis.