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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| Format: | Article |
| Language: | English |
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Tehran University of Medical Sciences
2019-06-01
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| Series: | Iranian Journal of Public Health |
| Subjects: | |
| Online Access: | https://ijph.tums.ac.ir/index.php/ijph/article/view/17340 |
| _version_ | 1818896418910240768 |
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| author | Ogunsakin ROPO EBENEZER Siaka LOUGUE |
| author_facet | Ogunsakin ROPO EBENEZER Siaka LOUGUE |
| author_sort | Ogunsakin ROPO EBENEZER |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-19T18:59:58Z |
| format | Article |
| id | doaj.art-ef5f8fd40d294c47846eb525abfc9b35 |
| institution | Directory Open Access Journal |
| issn | 2251-6085 2251-6093 |
| language | English |
| last_indexed | 2024-12-19T18:59:58Z |
| publishDate | 2019-06-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | Iranian Journal of Public Health |
| spelling | doaj.art-ef5f8fd40d294c47846eb525abfc9b352022-12-21T20:09:36ZengTehran University of Medical SciencesIranian Journal of Public Health2251-60852251-60932019-06-0148610.18502/ijph.v48i6.2901Bayesian Generalized Linear Mixed Modeling of Breast CancerOgunsakin ROPO EBENEZER0Siaka LOUGUE1Department of Statistics, School of Mathematics, Statistics, and Computer Science, University of Kwazulu Natal, Dur-ban,Department of Statistics, School of Mathematics, Statistics, and Computer Science, University of Kwazulu Natal, Dur-ban, South AfricaBackground: 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.https://ijph.tums.ac.ir/index.php/ijph/article/view/17340BayesianBreast cancerMultilevelGeneralized linear mixed modelingCODA/BOA |
| spellingShingle | Ogunsakin ROPO EBENEZER Siaka LOUGUE Bayesian Generalized Linear Mixed Modeling of Breast Cancer Iranian Journal of Public Health Bayesian Breast cancer Multilevel Generalized linear mixed modeling CODA/BOA |
| 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 | Bayesian Breast cancer Multilevel Generalized linear mixed modeling CODA/BOA |
| url | https://ijph.tums.ac.ir/index.php/ijph/article/view/17340 |
| work_keys_str_mv | AT ogunsakinropoebenezer bayesiangeneralizedlinearmixedmodelingofbreastcancer AT siakalougue bayesiangeneralizedlinearmixedmodelingofbreastcancer |