Bayesian adaptive design for clinical trials with potential subgroup effects.

Area of Science:

• Biostatistics
• Clinical Trial Design
• Bayesian Inference

Background:

• Adaptive clinical trial designs aim for efficiency but often rely on fixed assumptions about treatment efficacy and subgroup effects.
• Handling subgroup heterogeneity is crucial for personalized medicine but challenging with traditional trial designs.

Purpose of the Study:

• To develop a Bayesian adaptive design that explicitly models and learns from uncertainty in treatment efficacy and subgroup effects.
• To create a decision-theoretic framework for adaptively selecting the optimal testing strategy in clinical trials with potential subgroup heterogeneity.

Main Methods:

• A hierarchical mixture prior was used to represent uncertainty in overall efficacy and biomarker-defined subgroup effects.
• Interim data updated hyperparameters to posterior distributions, enabling adaptive strategy selection (overall, subgroup, or joint testing).
• An evidence-based alpha-splitting parameter maximized power under error constraints, solved using GPU-accelerated quasi-Monte Carlo integration.

Main Results:

• Simulation studies demonstrated the design maintains nominal error control across various subgroup prevalences and effect sizes.
• The proposed design achieved superior statistical power and decision accuracy compared to existing methods.
• The adaptive approach proved robust to prior misspecification.

Conclusions:

• This Bayesian adaptive design offers a transparent and computationally practical tool for confirmatory trials with uncertain subgroup effects.
• It supports precision medicine by enabling adaptive allocation of resources and Type I error based on emerging data.
• The framework promotes regulatory reproducibility and enhances decision-making in complex clinical trial scenarios.