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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.
Statistics in Medicine
|September 11, 2025
Summary
Identifying treatment effects in specific patient subgroups is crucial but challenging in randomized clinical trials (RCTs). This study introduces a novel hierarchical grouped horseshoe prior (HGHP) method for improved subgroup analysis and estimation.
Area of Science:
- Biostatistics
- Clinical Trial Methodology
- Statistical Genetics
Background:
- Randomized clinical trials (RCTs) often lack the statistical power to identify and estimate treatment effects within specific patient subgroups.
- Heterogeneity in treatment effects across subgroups is common, necessitating methods for subgroup identification and effect estimation.
Purpose of the Study:
- To introduce a novel hierarchical grouped horseshoe prior (HGHP) method designed for effective subgroup identification and effect estimation in clinical trials.
- To evaluate the performance of the HGHP method against existing shrinkage priors.
Main Methods:
- Development and application of a novel hierarchical grouped horseshoe prior (HGHP) Bayesian model.
- Simulation studies to compare HGHP performance with other shrinkage priors.
- Application of the HGHP method to a real-world COVID-19 clinical trial dataset.
Main Results:
- The proposed HGHP approach demonstrated superior positive predictive value compared to alternative shrinkage priors.
- HGHP resulted in narrower credible intervals, indicating more precise estimation of subgroup treatment effects.
- The method was successfully applied to analyze a COVID-19 clinical trial.
Conclusions:
- The HGHP offers a powerful and effective Bayesian approach for subgroup identification and effect estimation in clinical trials.
- This method addresses the limitations of traditional RCT power for subgroup analyses.
- The HGHP method shows promise for improving personalized medicine strategies by identifying differential treatment effects.
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