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Blinded sample size re-estimation in crossover bioequivalence trials.

Daniel Golkowski¹, Tim Friede, Meinhard Kieser

¹Institute of Medical Biometry and Informatics, University of Heidelberg, Im Neuenheimer Feld 305, D-69115 Heidelberg, Germany.

Pharmaceutical Statistics

|April 10, 2014

Summary

This summary is machine-generated.

This study introduces a blinded sample size re-estimation strategy for drug bioequivalence trials. While effective, it may inflate type I error rates, requiring adjustments for accurate significance level control.

Keywords:

bioequivalence crossover trials internal pilot study sample size re-estimation

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Area of Science:

Pharmacokinetics and Drug Development
Clinical Trial Design
Statistical Methods in Medicine

Background:

Bioequivalence studies are crucial in drug development to confirm formulation equivalence via bioavailability.
Crossover studies are standard for bioequivalence, but high variability in pharmacokinetic endpoints poses planning challenges.
Adaptive designs offer solutions by adjusting sample size based on accumulating data.

Purpose of the Study:

To propose and evaluate a blinded sample size re-estimation strategy for bioequivalence trials.
To investigate the type I error rates associated with the proposed blinded procedure.
To demonstrate methods for adjusting significance levels and improving power, especially for small sample sizes.

Main Methods:

Development of a blinded sample size re-estimation strategy for crossover bioequivalence studies.
Investigation of type I error rates under the proposed adaptive design.
Application of adjustment methods to control significance levels and enhance power.

Main Results:

The proposed blinded sample size re-estimation strategy can lead to an inflation of the type I error rate.
Adjustment methods are demonstrated to effectively control the type I error rate at a pre-specified level.
Refinements improve power, particularly in small sample size scenarios.

Conclusions:

Blinded sample size re-estimation in bioequivalence trials requires careful adjustment to maintain statistical integrity.
The proposed strategy, with adjustments, offers a viable approach to managing uncertainty in trial planning.
The refinements enhance the utility of adaptive designs for drug development, especially with limited participant numbers.