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Bayesian Generalized Linear Mixed Modeling of Breast Cancer

BACKGROUND: Breast cancer is one of the most common cancers among women. Breast cancer treatment strategies in Nigeria need urgent strengthening to reduce mortality rate because of the disease. This study aimed to determine the relationship between the ages at diagnosis and established the prognosti...

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Autores principales: ROPO EBENEZER, Ogunsakin, LOUGUE, Siaka
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Tehran University of Medical Sciences 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6635339/
https://www.ncbi.nlm.nih.gov/pubmed/31341845
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author ROPO EBENEZER, Ogunsakin
LOUGUE, Siaka
author_facet ROPO EBENEZER, Ogunsakin
LOUGUE, Siaka
author_sort ROPO EBENEZER, Ogunsakin
collection PubMed
description BACKGROUND: Breast cancer is one of the most common cancers among women. Breast cancer treatment strategies in Nigeria need urgent strengthening to reduce mortality rate because of the disease. This study aimed to determine the relationship between the ages at diagnosis and established the prognostic factors of modality of treatment given to breast cancer patient in Nigeria. METHODS: The data was collected for 247 women between years 2011–2015 who had breast cancer in two different hospitals in Ekiti State, Nigeria. Model estimation is based on Bayesian approach via Markov Chain Monte Carlo. A multilevel model based on generalized linear mixed model is used to estimate the random effect. RESULTS: The mean age of the patients (at the time of diagnosis) was 42.2 yr with 52% of the women aged between 35–49 yr. The results of the two approaches are almost similar but preference is given to Bayesian because the approach is more robust than the frequentist. Significant factors of treatment modality are age, educational level and breast cancer type. CONCLUSION: Differences in socio-demographic factors such as educational level and age at diagnosis significantly influence the modality of breast cancer treatment in western Nigeria. The study suggests the use of Bayesian multilevel approach in analyzing breast cancer data for the practicality, flexibility and strength of the method.
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spelling pubmed-66353392019-07-24 Bayesian Generalized Linear Mixed Modeling of Breast Cancer ROPO EBENEZER, Ogunsakin LOUGUE, Siaka Iran J Public Health Original Article BACKGROUND: Breast cancer is one of the most common cancers among women. Breast cancer treatment strategies in Nigeria need urgent strengthening to reduce mortality rate because of the disease. This study aimed to determine the relationship between the ages at diagnosis and established the prognostic factors of modality of treatment given to breast cancer patient in Nigeria. METHODS: The data was collected for 247 women between years 2011–2015 who had breast cancer in two different hospitals in Ekiti State, Nigeria. Model estimation is based on Bayesian approach via Markov Chain Monte Carlo. A multilevel model based on generalized linear mixed model is used to estimate the random effect. RESULTS: The mean age of the patients (at the time of diagnosis) was 42.2 yr with 52% of the women aged between 35–49 yr. The results of the two approaches are almost similar but preference is given to Bayesian because the approach is more robust than the frequentist. Significant factors of treatment modality are age, educational level and breast cancer type. CONCLUSION: Differences in socio-demographic factors such as educational level and age at diagnosis significantly influence the modality of breast cancer treatment in western Nigeria. The study suggests the use of Bayesian multilevel approach in analyzing breast cancer data for the practicality, flexibility and strength of the method. Tehran University of Medical Sciences 2019-06 /pmc/articles/PMC6635339/ /pubmed/31341845 Text en Copyright© Iranian Public Health Association & Tehran University of Medical Sciences http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
ROPO EBENEZER, Ogunsakin
LOUGUE, Siaka
Bayesian Generalized Linear Mixed Modeling of Breast Cancer
title Bayesian Generalized Linear Mixed Modeling of Breast Cancer
title_full Bayesian Generalized Linear Mixed Modeling of Breast Cancer
title_fullStr Bayesian Generalized Linear Mixed Modeling of Breast Cancer
title_full_unstemmed Bayesian Generalized Linear Mixed Modeling of Breast Cancer
title_short Bayesian Generalized Linear Mixed Modeling of Breast Cancer
title_sort bayesian generalized linear mixed modeling of breast cancer
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6635339/
https://www.ncbi.nlm.nih.gov/pubmed/31341845
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