Power prior for borrowing the real-world data in bioequivalence test with a parallel design

Area of Science:

• Pharmacokinetics and Drug Development
• Biostatistics
• Real-World Evidence (RWE)

Background:

• Real-world studies are increasingly vital in drug development.
• Existing bioequivalence trials can be inefficient, especially for drugs with long half-lives.
• Abundant real-world data from marketed reference drugs offer potential for trial optimization.

Purpose of the Study:

• To propose a Bayesian method for incorporating real-world data into bioequivalence trials.
• To enhance the statistical power and efficiency of bioequivalence studies.
• To reduce the required sample size for drugs with long half-lives.

Main Methods:

• Utilized a Bayesian approach with the power prior method to integrate real-world data.
• Employed average bioequivalence posterior probability for assessing drug equivalence.
• Conducted simulation studies to evaluate the method's performance across various scenarios.

Main Results:

• The proposed Bayesian design demonstrated higher statistical power compared to traditional methods.
• The method effectively controlled Type I error rates.
• Significant reductions in sample size and associated trial costs were observed.

Conclusions:

• Incorporating real-world data via the proposed Bayesian method improves bioequivalence trial efficiency.
• This approach offers a cost-effective strategy for drug development, particularly for long half-life drugs.
• The method provides a robust alternative for demonstrating bioequivalence while optimizing resource allocation.