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dc.contributor.authorRafati, S
dc.contributor.authorBaneshi, MR
dc.contributor.authorBahrampour, A
dc.date.accessioned2020-07-15T05:50:27Z
dc.date.available2020-07-15T05:50:27Z
dc.date.issued2020
dc.identifier.issn2476-762X
dc.identifier.doi10.31557/APJCP.2020.21.2.485
dc.identifier.urihttp://hdl.handle.net/10072/395438
dc.description.abstractBACKGROUND: 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.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherEpiSmart Science Vector Ltd
dc.relation.ispartofpagefrom485
dc.relation.ispartofpageto490
dc.relation.ispartofissue2
dc.relation.ispartofjournalAsian Pacific Journal of Cancer Prevention (APJCP)
dc.relation.ispartofvolume21
dc.subject.fieldofresearchClinical sciences
dc.subject.fieldofresearchOncology and carcinogenesis
dc.subject.fieldofresearchHealth services and systems
dc.subject.fieldofresearchPublic health
dc.subject.fieldofresearchcode3202
dc.subject.fieldofresearchcode3211
dc.subject.fieldofresearchcode4203
dc.subject.fieldofresearchcode4206
dc.subject.keywordsBayesian
dc.subject.keywordsCure models
dc.subject.keywordsbreast cancer
dc.subject.keywordssurvival
dc.titleFactors 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
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationRafati, S; Baneshi, MR; Bahrampour, A, 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, Asian Pacific Journal of Cancer Prevention (APJCP), 2020, 21 (2), pp. 485-490
dc.date.updated2020-07-15T05:48:07Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 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).
gro.hasfulltextFull Text
gro.griffith.authorBahrampour, Abbas


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