Risk Estimation for Lung Cancer in Libya: Analysis Based on Standardized Morbidity Ratio, Poisson-Gamma Model, BYM Model and Mixture Model

Abstract

<br /> <span style="font-size: small;">Cancer is the most rapidly spreading disease in the world, especially in developing countries, including Libya. Cancer </span><span style="font-family: Times New Roman,Times New Roman; font-size: small;"><span style="font-family: Times New Roman,Times New Roman; font-size: small;">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 </span></span><span style="font-size: small;">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. </span><span style="font-family: Times New Roman,Times New Roman; font-size: small;"><span style="font-family: Times New Roman,Times New Roman; font-size: small;">They are initially known as Standardized Morbidity Ratio, which is the most popular statistic, which used in the field </span></span><span style="font-size: small;">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 </span><span style="font-family: Times New Roman,Times New Roman; font-size: small;"><span style="font-family: Times New Roman,Times New Roman; font-size: small;">proposed models, which we then apply to lung cancer data in Libya. Maps, tables and graph, goodness-of-fit (GOF) </span></span><span style="font-size: small;">were used to compare and present the preliminary results. This GOF is common in statistical modelling to compare </span><span style="font-family: Times New Roman,Times New Roman; font-size: small;"><span style="font-family: Times New Roman,Times New Roman; font-size: small;">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 </span></span><span style="font-size: small;">certain districts. Results show that the Mixture model is most robust and provides better relative risk estimates across a range of models. </span>