Breast cancer is the second leading cause of death from cancer among women, and many factors are involved in its creation. The purpose of this study was to evaluate the effect of tumor markers on the survival of women with this cancer using Bayesian cure analysis.
This was a population-based cohort study on 500 women with breast cancer registered in Shahid Ramazanzadeh hospital, Yazd City, Iran, from the April 2010 until March 2015, using Kaplan-Meier method and Bayesian cure model. The data were analyzed using R software. P < 0.050 was considered as the significance level.
Based on Kaplan-Meier method, the 6-year cumulative survival for patients with breast cancer was 0.737. The mean age of breast cancer diagnosis was 48.03 ± 11.16 years, and the mean survival period was 97.64 ± 4.23 months. Bayesian cure model showed that Ki67 [hazard ratio (HR) = 1.34, 95% prediction interval (PI): 1.01-2.28] and ER (HR = 2.11, PI 95%: 1.99-2.36) were significantly related to hazard, and ER was significantly related to cure (OR = 0.38, PI 95%: 0.26-0.57).
According to Bayesian cure analysis in this study, ER variable is also effective on short-term survival and long-term survival of patients. Cure models have the ability to analyze patients’ survival data, and can differentiate long-term survival from short- term survival. The interpretation of survival data with these statistical models could be more accurate.