An Adaptive Biomarker-based Umbrella Trial Design Using Bayesian Latent Class Model

Area of Science:

• Biostatistics
• Clinical Trial Design
• Translational Medicine

Background:

• Identifying effective treatments for specific patient subgroups is crucial in precision medicine.
• Traditional clinical trial designs may lack efficiency in evaluating subgroup-specific treatment effects.
• Adaptive designs offer flexibility but require robust statistical frameworks.

Purpose of the Study:

• To propose a novel Bayesian adaptive umbrella trial design for identifying effective treatments in biomarker-defined subgroups.
• To enhance the efficiency and robustness of testing subgroup-specific treatment effects.
• To improve ethical considerations through response-adaptive randomization and early stopping rules.

Main Methods:

• Employs a Bayesian adaptive umbrella trial design.
• Utilizes a mixture regression model for joint analysis of binary and time-to-event outcomes.
• Introduces a latent class model for adaptive subgroup clustering and information borrowing.
• Incorporates response-adaptive randomization and futility/superiority stopping rules.

Main Results:

• The proposed design effectively identifies treatments for biomarker-defined subgroups.
• Adaptive clustering and information borrowing enhance statistical power and efficiency.
• Simulations confirm the design's desired performance characteristics.
• Response-adaptive randomization and early stopping rules improve trial ethics.

Conclusions:

• The Bayesian adaptive umbrella trial design offers a robust and efficient framework for precision medicine research.
• This design optimizes treatment allocation and decision-making in subgroup analyses.
• The approach holds significant potential for improving clinical trial methodology and patient outcomes.