Disease mapping of biomarkers for breast cancer in Tehran using spatial joint model: A Bayesian perspective

Abstract Breast cancer is one of the most important medical concerns that women face today. There are some biomarkers for detection of this cancer. Modeling these biomarkers, finding important factors that are associated with them and estimating the spatial pattern in disease risk across the areal units by disease mapping are the main foci of many studies. In this article, three binary biomarkers (the presence of estrogen receptors, the presence of progesterone receptors, and the absence of human epidermal growth factor receptor-2) are considered simultaneously for disease mapping of breast cancer. The association of these three biomarkers and spatial effects on them are jointly considered by using a convolution model. The proposed approach is applied to disease mapping of biomarkers of breast cancer in Tehran.