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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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Main Authors: Ahmed Alramah, Maryam, Nor Azah Samat,, Zulkifley Mohamed,
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2019
Online Access:http://journalarticle.ukm.my/13071/1/25%20Maryam%20Ahmed%20Alramah.pdf
http://journalarticle.ukm.my/13071/
http://www.ukm.my/jsm/malay_journals/jilid48bil1_2019/KandunganJilid48Bil1_2019.html
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spelling my-ukm.journal.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
institution Universiti Kebangsaan Malaysia
building Perpustakaan Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
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/13071/1/25%20Maryam%20Ahmed%20Alramah.pdf
http://journalarticle.ukm.my/13071/
http://www.ukm.my/jsm/malay_journals/jilid48bil1_2019/KandunganJilid48Bil1_2019.html
_version_ 1643738971027537920
score 13.211869

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