Combining Evidence from Clinical Trials in Conditional or Accelerated Approval

TL;DR

This paper evaluates the harmonic mean Chi-squared test for combining evidence from pre- and post-market clinical trials in drug approval, proposing it as a tool to optimize trial design and evidence assessment.

Contribution

It introduces the harmonic mean Chi-squared test as a novel method for evidence combination in accelerated drug approval, comparing it with traditional methods.

Findings

The harmonic mean Chi-squared test always requires a post-market trial.

It reduces the needed sample size when pre-market p-value is very small.

Simulation studies show its favorable operating characteristics.

Abstract

Conditional (European Medicines Agency) or accelerated (U.S. Food and Drug Administration) approval of drugs allow earlier access to promising new treatments that address unmet medical needs. Certain post-marketing requirements must typically be met in order to obtain full approval, such as conducting a new post-market clinical trial. We study the applicability of the recently developed harmonic mean Chi-squared test to this conditional or accelerated approval framework. The proposed approach can be used both to support the design of the post-market trial and the analysis of the combined evidence provided by both trials. Other methods considered are the two-trials rule, Fisher's criterion and Stouffer's method. In contrast to some of the traditional methods, the harmonic mean Chi-squared test always requires a post-market clinical trial. If the p-value from the pre-market clinical trial is << 0.025, a smaller sample size for the post-market clinical trial is needed than with the two-trials rule. For illustration, we apply the harmonic mean Chi-squared test to a drug which received conditional (and later full) market licensing by the EMA. A simulation study is conducted to study the operating characteristics of the harmonic mean Chi-squared test and two-trials rule in more detail. We finally investigate the applicability of these two methods to compute the power at interim of an ongoing post-market trial. These results are expected to aid in the design and assessment of the required post-market studies in terms of the level of evidence required for full approval.