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Design and inference for 3-stage bioequivalence testing with serial sampling data.

Fangrong Yan1, Huihong Zhu1, Junlin Liu1

  • 1Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, P.R. China.

Pharmaceutical Statistics
|May 5, 2018
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Summary
This summary is machine-generated.

New 3-stage designs improve bioequivalence testing by gradually increasing sample sizes, leading to more stable variance estimates and optimal sample sizes. This enhances statistical power and reliability in drug development.

Keywords:
bioequivalence testingsample size estimationsequential designserial sampling datastatistical power

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Area of Science:

  • Pharmacokinetics and Pharmaceutical Sciences
  • Biostatistics and Clinical Trial Design

Background:

  • Bioequivalence tests compare drug bioavailability (Cmax, AUC) between test and reference products.
  • Current 2-stage designs rely on initial variance estimates for sample size determination, which can be unstable.
  • Serial sampling schedules in bioequivalence studies exacerbate variance estimation challenges.

Purpose of the Study:

  • To propose novel 3-stage designs for bioequivalence testing.
  • To address the limitations of unstable variance estimation in 2-stage designs, particularly with serial sampling.
  • To enhance the accuracy of sample size estimation and improve statistical power.

Main Methods:

  • Development and simulation of 3-stage bioequivalence designs.
  • Gradual increase of sample sizes across stages to refine variance estimates.
  • Adjustment of significance levels to maintain overall Type I error rates.

Main Results:

  • Proposed 3-stage designs provide more stable variance estimates compared to 2-stage designs.
  • Gradual sample size increases prevent excessively large sample sizes.
  • Simulations demonstrate increased statistical power and reliable sample size determination.

Conclusions:

  • 3-stage designs offer a more robust approach to bioequivalence testing, especially with serial sampling.
  • These designs improve the efficiency and reliability of sample size calculations.
  • The proposed methodology enhances the precision of bioavailability parameter comparisons.