Combining evidence from clinical trials in conditional or accelerated approval

Area of Science:

• Biostatistics
• Pharmaceutical Sciences
• Regulatory Science

Background:

• Conditional or accelerated drug approval allows early patient access to novel therapies for unmet medical needs.
• Full drug approval typically requires fulfilling post-marketing commitments, often including new clinical trials.
• Evaluating statistical methods for these post-approval processes is crucial for drug development.

Purpose of the Study:

• To assess the applicability of the harmonic mean test in the context of conditional/accelerated drug approval.
• To explore the use of the harmonic mean test for designing post-market trials and analyzing combined evidence.
• To compare the harmonic mean test with traditional methods like the two-trials rule, Fisher's criterion, and Stouffer's method.

Main Methods:

• The study investigates the harmonic mean test, a recently developed statistical approach.
• It compares the harmonic mean test with established methods including the two-trials rule.
• A simulation study is conducted to evaluate operating characteristics, and the methods are applied to a real-world drug approval case.

Main Results:

• The harmonic mean test, unlike some traditional methods, mandates a post-market clinical trial.
• It can require a smaller post-market sample size than the two-trials rule when the pre-market p-value is sufficiently small.
• The study demonstrates the application of the harmonic mean test to a drug approved by the European Medicines Agency.

Conclusions:

• The harmonic mean test is a viable statistical tool for the conditional/accelerated drug approval framework.
• It offers potential advantages in sample size reduction for post-market trials compared to the two-trials rule.
• The findings support the use of the harmonic mean test in designing and assessing post-market studies for full drug approval.