Related Experiment Video
Updated: Jan 18, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
Hierarchical Grouped Horseshoe Priors for Subgroup Identification and Estimation
Ethan M Alt1, Anil Anderson1, Qing Li2
1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Abstract:
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.
Related Concept Videos
Sampling Plans
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Friedman Two-way Analysis of Variance by Ranks
In- and Out-Groups
Stratified Sampling Method
To choose a stratified sample, divide the population into groups called strata and then take a...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...
Comparing the Survival Analysis of Two or More Groups

