Alhdiri Maryam Ahmed, Samat Nor Azah, Mohamed Zulkifley
Department of Statistics, Faculty of Science, University of Tripoli, Alfernag Tripoli, Libya. Email:
Asian Pac J Cancer Prev. 2017 Mar 1;18(3):673-679. doi: 10.22034/APJCP.2017.18.3.673.
Cancer is the most rapidly spreading disease in the world, especially in developing countries, including Libya. Cancer represents a significant burden on patients, families, and their societies. This disease can be controlled if detected early. Therefore, disease mapping has recently become an important method in the fields of public health research and disease epidemiology. The correct choice of statistical model is a very important step to producing a good map of a disease. Libya was selected to perform this work and to examine its geographical variation in the incidence of lung cancer. The objective of this paper is to estimate the relative risk for lung cancer. Four statistical models to estimate the relative risk for lung cancer and population censuses of the study area for the time period 2006 to 2011 were used in this work. They are initially known as Standardized Morbidity Ratio, which is the most popular statistic, which used in the field of disease mapping, Poisson-gamma model, which is one of the earliest applications of Bayesian methodology, Besag, York and Mollie (BYM) model and Mixture model. As an initial step, this study begins by providing a review of all proposed models, which we then apply to lung cancer data in Libya. Maps, tables and graph, goodness-of-fit (GOF) were used to compare and present the preliminary results. This GOF is common in statistical modelling to compare fitted models. The main general results presented in this study show that the Poisson-gamma model, BYM model, and Mixture model can overcome the problem of the first model (SMR) when there is no observed lung cancer case in certain districts. Results show that the Mixture model is most robust and provides better relative risk estimates across a range of models.
癌症是世界上传播速度最快的疾病，在包括利比亚在内的发展中国家尤为如此。癌症给患者、家庭及其社会带来了沉重负担。如果能早期发现，这种疾病是可以得到控制的。因此，疾病地图绘制最近已成为公共卫生研究和疾病流行病学领域的一种重要方法。正确选择统计模型是绘制出良好疾病地图的非常重要的一步。选择利比亚来开展这项工作，并研究其肺癌发病率的地理差异。本文的目的是估计肺癌的相对风险。本研究使用了四种估计肺癌相对风险的统计模型以及2006年至2011年研究区域的人口普查数据。它们最初分别是标准化发病比（这是疾病地图绘制领域最常用的统计量）、泊松 - 伽马模型（这是贝叶斯方法的早期应用之一）、贝萨格、约克和莫利（BYM）模型以及混合模型。作为第一步，本研究首先对所有提出的模型进行综述，然后将其应用于利比亚的肺癌数据。使用地图、表格、图表以及拟合优度（GOF）来比较并呈现初步结果。这种拟合优度在统计建模中很常见，用于比较拟合模型。本研究呈现的主要总体结果表明，当某些地区没有观察到肺癌病例时，泊松 - 伽马模型、BYM模型和混合模型可以克服第一个模型（标准化发病比）的问题。结果表明，混合模型最为稳健，并且在一系列模型中能提供更好的相对风险估计。
