Factors Affecting Long-Survival of Patients with Breast Cancer by Non-Mixture and Mixture Cure Models Using the Weibull, Log-logistic and Dagum Distributions: A Bayesian Approach.

Shideh Rafati,Mohammad Reza Baneshi,Abbas Bahrampour

doi:10.31557/apjcp.2020.21.2.485

Shideh Rafati, Mohammad Reza Baneshi + Show 1 more

Open Access

https://doi.org/10.31557/apjcp.2020.21.2.485

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Abstract

Background:Breast cancer is a top biomedical research priority, and it is a major health problem. Therefore, the present study aimed to determine the prognostic factors of breast cancer survival using cure models. Methods:In this retrospective cohort analytic study, data of 140 breast cancer patients were collected from Ali Ibn Abi Taleb hospital, Rafsanjan, Southeastern Iran. Since in this study, a part of the population had long-term survival, cure models were used and evaluated using DIC index. The data were analyzed using Openbugs Software. Results:In this study, of 140 breast cancer patients, 23 (16.4%) cases died of breast cancer. Based on the findings, the Bayesian nonmixture cure model, with type I Dagum distribution, was the best fitted model. The variables of BMI, number of children, number of natural deliveries, tumor size, metastasis, consumption of canned food, tobacco use, and breastfeeding affected patients’ survival based on type I Dagum distribution. Conclusion:The results of the present study demonstrated that the Bayesian nonmixture cure model, with type I Dagum distribution, can be a good model to determine factors affecting the survival of patients when there is the possibility of a fraction of cure. In this study, it was found that adapting a healthy lifestyle (eg, avoiding canned foods and smoking) can improve the survival of breast cancer patients.

Highlights

Breast cancer is the most common cancer affecting women worldwide, and its incidence and mortality rates are expected to significantly increase in the future (Anastasiadi et al, 2017)
The results of the present study demonstrated that the Bayesian nonmixture cure model, with type I Dagum distribution, can be a good model to determine factors affecting the survival of patients when there is the possibility of a fraction of cure
The aim of this study was to assess and compare Bayesian mixture and nonmixture cure fraction models based on the Weibull, Log-logistic, and Dagum distributions and determine factors affecting the survival of breast cancer patients

Summary

Introduction

Breast cancer is the most common cancer affecting women worldwide, and its incidence and mortality rates are expected to significantly increase in the future (Anastasiadi et al, 2017). The mean age of Iranian breast cancer patients is 30.0 years; these patients are diagnosed with cancer at least a decade earlier compared with the developed countries(Abedi et al, 2016; Rahimzadeh et al, 2016). As this cancer occurs progressively in women worldwide, it is of paramount importance to identify factors that affect the survival of breast cancer patients. Conclusion: The results of the present study demonstrated that the Bayesian nonmixture cure model, with type I Dagum distribution, can be a good model to determine factors affecting the survival of patients when there is the possibility of a fraction of cure. It was found that adapting a healthy lifestyle (eg, avoiding canned foods and smoking) can improve the survival of breast cancer patients

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