Evaluation of the Influential Factors Affecting Survival of the Patients with Breast Cancer Using Bayesian Method

Background and Aim: Breast cancer is the most important cause of cancer death in women. The purpose of this study was to evaluate the effect of Estrogen Receptor (ER), Human Epidermal Growth Receptor (HER2) and other factors on post-surgical survival of the patients with breast cancer using Bayesian approach for parametric proportional hazards model. Materials and Methods: This was a retrospective study. Data of 165 breast cancer patients who had undergone surgery at Ahvaz Healing Diagnostic Center from 2004 to 2014 were recorded in a data collection form. The variables of age, tumor size, number of lymph nodes involved, cancer grade, ER status and HER2 status were evaluated. Survival time was calculated from the date of surgery to the date of death or study end date (September 2015), in months. In the Bayesian approach in parametric survival analysis models with proportional hazards, the lateral distribution of parameters was estimated using MCMC method. Also, we evaluated efficiency of the models using the deviance information criterion. All data analysis steps were performed by using Stata15 software. Significance coefficients of the model were determined using the 95% credible interval. Results: The mean and standard deviation of age were 46.40 and 9.94 years, respectively. Deviance information criterion for Weibull parametric model was lower than those of other parametric models. Based on the Bayesian estimation of the Weibull's proportional hazards parametric model, tumor size (HR = 1.40), the number of involved lymph nodes (HR = 1.016), Ki67 status (HR = 1.115), tumor grade (HR = 1.022), HER2 status (HR = 1.760) and ER status (HR = 1.381) had a positive effect on risk of death. Age had a negative effect on risk of death (HR=0.978). Conclusion: Based on the Bayesian proportional hazards Weibull model, tumor size, the number of involved lymph nodes, Ki67, tumor's grade, HER2 and ER had a positive effect on the risk of death.