Bayesian analysis of Gastric Cancer rate in Gilan Province by using the Auto-beta binomial model

The climatic and environmental conditions in each region contribute to the outbreak of certain diseases. Therefore, providing a map of the event rate of a disease or mortality from various diseases on a geographic area is one issue of concern for physicians and health experts. Considering that gastric cancer is the most common cancer in Gilan province, Iran, in this paper, we study the impact of some risk factors on the rate of this cancer for the cities of Gilan province by using two auto-binomial and auto-beta-binomial Bayesian spatial models. The other purposes of this study are providing the gastric cancer rate prediction map and comparing the performance of the two proposed models. We used a dataset from the Razi Educational Center of Rasht in which the data were collected for sixteen cities of Gilan during the period of 2012-2017. We fitted the proposed models for these data by using an approximate Bayesian approach, called the integrated nested Laplace approximation (INLA). Based on the results, it was found that prediction of the rates of cancer in most of the cities of Gilan are similar by using of both models; in cities where there is a difference, the auto-binomial model predicts a higher rate than the auto-beta-binomial model. The reason for this is also that the auto-binomial model is over-fitted, which reduces its ability to predict.