Determination of the Risk Factors for Breast Cancer Survival Using the Bayesian Method, Yazd, Iran Publisher

Summary: Bayesian modeling reveals surgery and tumor markers significantly impact breast cancer survival rates. Can this guide treatment choices effectively? #BreastCancer #SurvivalAnalysis

Pahlevani V ; Mohamadzade M ; Pahlevani N ; Zadeh VN
Source: Advanced Biomedical Research Published:2021

Abstract

Background: There are numerous sophisticated studies which have investigated risk factors of breast cancer (BC). The purpose of this paper is to use benefits of Bayesian modeling to involve such prior information in determining factors affecting the survival of women with BC in Yazd city. Materials and Methods: The checklist included the characteristics of the patients and the factors studied. Then, from the records of patients referred to Radiotherapy Center of Shahid Ramezanzadeh, who had BC, from April 2005 to March 2012, the survival of 538 persons was recorded in the census. Data were analyzed by R software version 3.4.2, and 0.05 was considered the significance level. Results: The mean age of BC diagnosis was 48.03 ± 11016 years. The Bayesian Cox regression showed that surgery (hazard ratio [HR] =1.631 95% PI; 1.102–2.422), ki67 (HR = 3.260. 95% PI; 1.6308–6.372), stage (HR = 5.620, 95% PI; 4.079–7.731), lymph node (HR = 1.765, 95% PI; 1.127–2.790), and ER (HR = 2. 600 95% PI; 2.023–3.354) were significantly related to survival time. Conclusion: The parametric and cox models were compared with standard error, and Cox model was selected as an optimal model. Accordingly, stage, ki67, lymph node, ER, and surgery variables had a positive effect on death hazard. © 2023 Elsevier B.V., All rights reserved.