A Data Augmentation Prior in Fractional Polynomial Generalized Linear Models

In microbial and chemical risk assessments, careful dose-response modeling is emphasized because a target risk level (probability of infection or illness) is often in the low benchmark response range of 1% to 10%. To address model uncertainty at low doses and enhance diversity and flexibility for model-averaging, a set of fractional polynomial dose-response models can be considered. However, elicitation of an informative prior in Bayesian approach is difficult in those models because their parameters are not interpretable. This paper illustrates a method of elicitating informative prior known using data augmentation prior.