Hierarchical modeling of risk factors with and without prior information-the process of regression model evaluation for an example of respiratory diseases in piglet production from daily practice data.

In veterinary epidemiology, regression models are commonly used to describe animal health and related risk factors. However, model selection and evaluation present ongoing challenges-especially when many potential predictors, complex interactions, and limited sample sizes are involved. The VASIB project serves as a representative example, focusing on piglet-producing farms with persistent respiratory disease problems. Across 30 farms, a wide array of variables was collected at the farm, barn, compartment, pen, and individual animal levels, aiming to support optimized treatment and management strategies to improve respiratory health. This study investigates the occurrence of coughing in pigs using various epidemiological models, including hierarchical frequentist logistic regression, non-hierarchical Bayesian logistic regression (with full and partial pooling), and hierarchical Bayesian models with informative and non-informative priors. These approaches are evaluated and compared using statistical measures such as the corrected Akaike Information Criterion (AIC