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Flexible Conditional Borrowing Approaches for Leveraging Historical Data in the Bayesian Design of Superiority Trials

Author

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  • Wenlin Yuan

    (University of Connecticut at Storrs)

  • Ming-Hui Chen

    (University of Connecticut at Storrs)

  • John Zhong

    (REGENXBIO Inc)

Abstract

In this paper, we consider the Bayesian design of a randomized, double-blind, placebo-controlled superiority clinical trial. To leverage multiple historical datasets to augment the placebo-controlled arm, we develop three conditional borrowing approaches built upon the borrowing-by-parts prior, the hierarchical prior, and the robust mixture prior. The operating characteristics of the conditional borrowing approaches are examined. Extensive simulation studies are carried out to empirically demonstrate the superiority of the conditional borrowing approaches over the unconditional borrowing or no-borrowing approaches in terms of controlling type I error, maintaining good power, having a large “sweet-spot” region, minimizing bias, and reducing the mean-squared error of the posterior estimate of the mean parameter of the placebo-controlled arm. Computational algorithms are also developed for calculating the Bayesian type I error and power as well as the corresponding simulation errors.

Suggested Citation

  • Wenlin Yuan & Ming-Hui Chen & John Zhong, 2022. "Flexible Conditional Borrowing Approaches for Leveraging Historical Data in the Bayesian Design of Superiority Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(2), pages 197-215, July.
    • Handle: RePEc:spr:stabio:v:14:y:2022:i:2:d:10.1007_s12561-021-09321-7
    DOI: 10.1007/s12561-021-09321-7
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    References listed on IDEAS

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    1. Ming-Hui Chen & Joseph G. Ibrahim & Donglin Zeng & Kuolung Hu & Catherine Jia, 2014. "Bayesian design of superiority clinical trials for recurrent events data with applications to bleeding and transfusion events in myelodyplastic syndrome," Biometrics, The International Biometric Society, vol. 70(4), pages 1003-1013, December.
    2. Ming-Hui Chen & Joseph G. Ibrahim & Peter Lam & Alan Yu & Yuanye Zhang, 2011. "Bayesian Design of Noninferiority Trials for Medical Devices Using Historical Data," Biometrics, The International Biometric Society, vol. 67(3), pages 1163-1170, September.
    3. Haitao Pan & Ying Yuan & Jielai Xia, 2017. "A calibrated power prior approach to borrow information from historical data with application to biosimilar clinical trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(5), pages 979-996, November.
    4. Brian P. Hobbs & Bradley P. Carlin & Sumithra J. Mandrekar & Daniel J. Sargent, 2011. "Hierarchical Commensurate and Power Prior Models for Adaptive Incorporation of Historical Information in Clinical Trials," Biometrics, The International Biometric Society, vol. 67(3), pages 1047-1056, September.
    5. Joseph G. Ibrahim & Ming-Hui Chen & H. Amy Xia & Thomas Liu, 2012. "Bayesian Meta-Experimental Design: Evaluating Cardiovascular Risk in New Antidiabetic Therapies to Treat Type 2 Diabetes," Biometrics, The International Biometric Society, vol. 68(2), pages 578-586, June.
    6. Heinz Schmidli & Sandro Gsteiger & Satrajit Roychoudhury & Anthony O'Hagan & David Spiegelhalter & Beat Neuenschwander, 2014. "Robust meta-analytic-predictive priors in clinical trials with historical control information," Biometrics, The International Biometric Society, vol. 70(4), pages 1023-1032, December.
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