Bayesian hierarchical model for heterogeneity in the diagnosis and treatment of hepatocellular carcinoma (HCC) in the African continent

HCC prevalence and etiology differ between African regions. To better understand these differences, a survey was conducted to characterize the health care practices in HCC management in Africa. Due to limited number of respondents from certain countries in Africa and due to scarce availability of resources, the traditional method of adaptive quadrature produces unstable estimates in this complex setting with sparse multinomial outcomes and with sparse clustered random effects. We propose a Bayesian hierarchical model to address this challenge, apply it to the sparse real data captured in the survey, and validate the proposed method through simulations on synthetic data.