A Bayesian hierarchical model for disease mapping that accounts for scaling and heavy-tailed latent effects.

ABSTRACT: In disease mapping, the relative risk of a disease is commonly estimated across different areas within a region of interest. The number of cases in an area is often assumed to follow a Poisson distribution whose mean is decomposed as the product between an offset and the logarithm of the disease's relative risk. The log risk may be written as the sum of fixed effects and latent random effects. A modified Besag-York-Mollié (BYM2) model decomposes each latent effect into a weighted sum of independent and spatial effects. We build on the BYM2 model to allow for heavy-tailed latent effects and accommodate potentially outlying risks, after accounting for the fixed effects. We assume a scale mixture structure wherein the variance of the latent process changes across areas and allows for outlier

SUBMITTER: Michal V 

PROVIDER: S-EPMC11874469 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature