Refining Subgroup Analysis in Clinical Trials: A Bayesian Hierarchical Approach

Music City Center 

Subgroup analysis is crucial in clinical trials for understanding how different subgroups respond to treatments and identifying patient characteristics that may influence efficacy or safety. However, it faces limitations, including small sample sizes, increased variability, and the risk of false discoveries due to multiple comparisons. These challenges often result in unreliable conclusions and hinder the generalization of findings.
Bayesian Hierarchical Modeling (BHM) with shrinkage estimation addresses these issues by modeling subgroup-specific parameters as deviations from the population parameter, allowing information to be shared across subgroups. This hierarchical structure stabilizes estimates by pulling extreme values toward more plausible estimates derived from the overall data, improving precision, reducing bias, and enhancing generalizability (Pennello G. et al,). It also quantifies uncertainty, providing clearer insights into treatment effects and supporting better decision-making particularly in the presence of unbalanced data.
The concept of BHM and its implementation in subgroup analysis will be discussed in detail with a simulated example from oncology trial.

Bayesian inference

shrinkage estimation

subgroup analysis 

Section on Bayesian Statistical Science