Hierarchical Grouped Horseshoe Priors for Subgroup Identification and Estimation

A common issue in randomized clinical trials (RCTs) is the identification of subgroups and the estimation of their effects. Typically, RCTs are not powered to estimate the effects of subgroups. However, in some circumstances, treatment may work for some groups and not others, and it is of interest to identify these subgroups and estimate their treatment effects. In this paper, we introduce a novel hierarchical grouped horseshoe prior (HGHP) for subgroup identification and estimation. We show via simulation that our proposed approach yields superior positive predictive value and narrower credible intervals compared to other shrinkage priors. We apply our method to a real clinical trial for COVID-19.