Survival Analysing of the Breast Cancer Patients Using Cure Model

Abstract

Background: Breast cancer (BC) is the most leading cause of cancer and the second most common cause of cancer-related death among females worldwide. The survival time of the disease and its risk factors are important for physicians.

Bayesian and Frequentist Analytical Approaches Using Log-Normal and Gamma Frailty Parametric Models for Breast Cancer Mortality

“…As noted, the frailty term addition in the model did not change the direction of the effect of the covariates and at best the estimates remained the same throughout the models, and substantive conclusions from the Weibull Standard models remain valid. Consistent with findings from other studies that applied both frequentist [18,40] and Bayesian approach [36,41] separately, the presence of heterogeneity in our study acting at the region level, as determined by the two approaches, varied. Gohari et al [18] in Iran reported the frailty relative risk of 7.2 in patients with breast cancer, while Gorfine et al [42] established that accounting for heterogeneity improved the ability of the model to predict the risk of developing a disease over time based on carrier status.…”

“…ough our approach is complex, the results obtained are consistent with what has been reported elsewhere. Similar to this study, metastasis increased the effects on the hazard of the event in a study conducted in Iran [40].…”

“…Gohari et al [18] in Iran reported the frailty relative risk of 7.2 in patients with breast cancer, while Gorfine et al [42] established that accounting for heterogeneity improved the ability of the model to predict the risk of developing a disease over time based on carrier status. Bakhshi et al [40] proved that cure and frailty models were better than the Cox model to estimate patient survival probabilities.…”

“…On the other hand, the presence of unobserved effects in this study reflects a different level of factors that can be categorized as genetic or behavioral, occurring at individual and regional levels, which may not have been measured. We are, however, confident that the other factors that were not considered but helped to reduce the unobserved household effect among cancer mortality risk may have details of specific family history, education background, type of treatment, and progesterone receptor (PR) [40,43] among other factors.…”

Application of additive hazards models for analyzing survival of breast cancer patients

“…Many studies were made on survival of Iranian BC patients. [ 12 13 18 24 25 ] The overall relative survival in Iranian BC patients was reported higher than some Asian and eastern European countries and was lower than the U.S. and western European countries. [ 25 ]…”