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

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Author(s)
Rafati, S
Baneshi, MR
Bahrampour, A
Griffith University Author(s)
Year published
2020
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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 ...
View more >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.
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View more >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.
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Journal Title
Asian Pacific Journal of Cancer Prevention (APJCP)
Volume
21
Issue
2
Copyright Statement
© The Author(s) 2020. The attached file is reproduced here in accordance with the copyright policy of the publisher. For information about this journal please refer to the journal’s website or contact the author(s).
Subject
Clinical sciences
Oncology and carcinogenesis
Health services and systems
Public health
Bayesian
Cure models
breast cancer
survival