Mapping lung cancer disease in Libya using Standardized Morbidity Ratio, BYM model and mixture model, 2006 to 2011: Bayesian Epidemiological Study
Cancer represents a significant burden on both patients and their families and their societies, especially in developing countries, including Libya. Therefore, the aim of this study was to model the geographical distribution of lung cancer incidence in Libya. The correct choice of a statistical mode...
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Penerbit Universiti Kebangsaan Malaysia
2019
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| Online Access: | http://journalarticle.ukm.my/13071/ http://journalarticle.ukm.my/13071/ http://journalarticle.ukm.my/13071/1/25%20Maryam%20Ahmed%20Alramah.pdf |
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ukm-130712019-06-20T14:52:37Z http://journalarticle.ukm.my/13071/ Mapping lung cancer disease in Libya using Standardized Morbidity Ratio, BYM model and mixture model, 2006 to 2011: Bayesian Epidemiological Study Ahmed Alramah, Maryam Nor Azah Samat, Zulkifley Mohamed, Cancer represents a significant burden on both patients and their families and their societies, especially in developing countries, including Libya. Therefore, the aim of this study was to model the geographical distribution of lung cancer incidence in Libya. The correct choice of a statistical model is a very important step to producing a good map of disease in question. Therefore, in this study will use three models to estimate the relative risk for lung cancer disease, they are initially Standardized Morbidity Ratio, which is the most common statistic used in disease mapping, BYM model, and Mixture model. As an initial step, this study begins by providing a review of all models are proposed, which we then apply to lung cancer data in Libya. In this paper, we show some preliminary results, which are displayed and compared by using maps, tables, graphics and goodness-of-fit, the last measure of displaying the results is common in statistical modelling to compare fitted models. The main general results presented in this study show that the last two models, BYM and Mixture have been demonstrated to overcome the problem of the first model when there no observed lung cancer cases in certain districts. Also, other results show that Mixture model is most robust and gives a better relative risk estimate across compared it with a range of models. Penerbit Universiti Kebangsaan Malaysia 2019-01 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/13071/1/25%20Maryam%20Ahmed%20Alramah.pdf Ahmed Alramah, Maryam and Nor Azah Samat, and Zulkifley Mohamed, (2019) Mapping lung cancer disease in Libya using Standardized Morbidity Ratio, BYM model and mixture model, 2006 to 2011: Bayesian Epidemiological Study. Sains Malaysiana, 48 (1). pp. 217-225. ISSN 0126-6039 http://www.ukm.my/jsm/malay_journals/jilid48bil1_2019/KandunganJilid48Bil1_2019.html |
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Digital Repository |
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Local University |
| institution |
Universiti Kebangasaan Malaysia |
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UKM Institutional Repository |
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Online Access |
| language |
English |
| description |
Cancer represents a significant burden on both patients and their families and their societies, especially in developing countries, including Libya. Therefore, the aim of this study was to model the geographical distribution of lung cancer incidence in Libya. The correct choice of a statistical model is a very important step to producing a good map of disease in question. Therefore, in this study will use three models to estimate the relative risk for lung cancer disease, they are initially Standardized Morbidity Ratio, which is the most common statistic used in disease mapping, BYM model, and Mixture model. As an initial step, this study begins by providing a review of all models are proposed, which we then apply to lung cancer data in Libya. In this paper, we show some preliminary results, which are displayed and compared by using maps, tables, graphics and goodness-of-fit, the last measure of displaying the results is common in statistical modelling to compare fitted models. The main general results presented in this study show that the last two models, BYM and Mixture have been demonstrated to overcome the problem of the first model when there no observed lung cancer cases in certain districts. Also, other results show that Mixture model is most robust and gives a better relative risk estimate across compared it with a range of models. |
| format |
Article |
| author |
Ahmed Alramah, Maryam Nor Azah Samat, Zulkifley Mohamed, |
| spellingShingle |
Ahmed Alramah, Maryam Nor Azah Samat, Zulkifley Mohamed, Mapping lung cancer disease in Libya using Standardized Morbidity Ratio, BYM model and mixture model, 2006 to 2011: Bayesian Epidemiological Study |
| author_facet |
Ahmed Alramah, Maryam Nor Azah Samat, Zulkifley Mohamed, |
| author_sort |
Ahmed Alramah, Maryam |
| title |
Mapping lung cancer disease in Libya using Standardized Morbidity Ratio, BYM model and mixture model, 2006 to 2011: Bayesian Epidemiological Study |
| title_short |
Mapping lung cancer disease in Libya using Standardized Morbidity Ratio, BYM model and mixture model, 2006 to 2011: Bayesian Epidemiological Study |
| title_full |
Mapping lung cancer disease in Libya using Standardized Morbidity Ratio, BYM model and mixture model, 2006 to 2011: Bayesian Epidemiological Study |
| title_fullStr |
Mapping lung cancer disease in Libya using Standardized Morbidity Ratio, BYM model and mixture model, 2006 to 2011: Bayesian Epidemiological Study |
| title_full_unstemmed |
Mapping lung cancer disease in Libya using Standardized Morbidity Ratio, BYM model and mixture model, 2006 to 2011: Bayesian Epidemiological Study |
| title_sort |
mapping lung cancer disease in libya using standardized morbidity ratio, bym model and mixture model, 2006 to 2011: bayesian epidemiological study |
| publisher |
Penerbit Universiti Kebangsaan Malaysia |
| publishDate |
2019 |
| url |
http://journalarticle.ukm.my/13071/ http://journalarticle.ukm.my/13071/ http://journalarticle.ukm.my/13071/1/25%20Maryam%20Ahmed%20Alramah.pdf |
| first_indexed |
2023-09-18T20:04:02Z |
| last_indexed |
2023-09-18T20:04:02Z |
| _version_ |
1777407038172692480 |