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

Huang Lei, Su Liwen, Zheng Yuling, Chen Yuanyuan and Yan Fangrong ()
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Huang Lei: Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 210009, China
Su Liwen: Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 210009, China
Zheng Yuling: Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 210009, China
Chen Yuanyuan: Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 210009, China
Yan Fangrong: Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 210009, China

The International Journal of Biostatistics, 2022, vol. 18, issue 1, 73-82

Abstract: Recently, real-world study has attracted wide attention for drug development. In bioequivalence study, the reference drug often has been marketed for many years and accumulated abundant real-world data. It is therefore appealing to incorporate these data in the design to improve trial efficiency. In this paper, we propose a Bayesian method to include real-world data of the reference drug in a current bioequivalence trial, with the aim to increase the power of analysis and reduce sample size for long half-life drugs. We adopt the power prior method for incorporating real-world data and use the average bioequivalence posterior probability to evaluate the bioequivalence between the test drug and the reference drug. Simulations were conducted to investigate the performance of the proposed method in different scenarios. The simulation results show that the proposed design has higher power than the traditional design without borrowing real-world data, while controlling the type I error. Moreover, the proposed method saves sample size and reduces costs for the trial.

Keywords: bioequivalence test; long half-life drug; power prior; real-world data (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/ijb-2020-0119

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