A flexible Bayesian non-confounding spatial model for analysis of dispersed count data in clinical studies
用于临床离散度计数数据分析的灵活贝叶斯非混杂空间模型
In employing spatial regression models for counts, we usually meet two issues. First, ignoring the inherent collinearity between covariates and the spatial effect would lead to causal inferences. Second, real count data usually reveal over or under-dispersion where the classical Poisson model is not appropriate to use. We propose a flexible Bayesian hierarchical modeling approach by joining non-confounding spatial methodology and a newly reconsidered dispersed count modeling…