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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Tehran University of Medical Sciences
2019
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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. |
format | Online Article Text |
id | pubmed-6635339 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Tehran University of Medical Sciences |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT ropoebenezerogunsakin bayesiangeneralizedlinearmixedmodelingofbreastcancer AT louguesiaka bayesiangeneralizedlinearmixedmodelingofbreastcancer |