A model-based approach for simulating adaptive clinical studies with surrogate endpoints used for interim...

Area of Science:

• Clinical Trials Methodology
• Biostatistics
• Pharmaceutical Research

Background:

• Adaptive clinical trial designs are crucial for optimizing dose selection.
• Interim analysis often uses surrogate endpoints when primary outcomes are delayed.
• Bayesian methods offer a flexible framework for complex trial designs.

Purpose of the Study:

• To propose a Bayesian model-based approach for adaptive clinical trial designs.
• To leverage historical data and surrogate endpoints for early dose selection.
• To investigate the operating characteristics of such designs.

Main Methods:

• Developed a Bayesian model incorporating a correlation between surrogate and primary endpoints.
• Utilized historical data to inform the correlation model.
• Employed simulation studies to evaluate design performance.

Main Results:

• The proposed Bayesian approach effectively utilizes surrogate endpoint information.
• Historical data incorporation improves the reliability of dose selection.
• Simulation studies demonstrate the method's utility for operating characteristic investigation.

Conclusions:

• The Bayesian model-based adaptive design is a viable strategy for efficient clinical trials.
• This approach facilitates early identification and dropping of suboptimal doses.
• The method is applicable for power and sample size calculations in clinical trial planning.