Benchmarking Bayesian subgroup shrinkage methods on clinical data

Music City Center 

This talk will compare different ways of implementing Bayesian shrinkage estimation for subgroup analysis on clinical trials data. Traditionally Bayesian shrinkage is applied to non-overlapping subgroups using hierarchical models. This implies that several models need to be fitted when several subgroup defining variables are of interest. Recently Wolbers et al (2024) propose to use a single global regression model using shrinkage priors for the used model.
We will compare the performance of different shrinkage approaches based on a real data benchmark. The evaluated approaches include no and full-shrinkage towards the overall treatment effect, Bayesian hierarchical shrinkage and more novel priors such as the global model prior R2D2 proposed by Zhang et al (2020) will be compared.

Bayesian