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Bayesian Design for Identifying Cohort-Specific Optimal Dose Combinations Based on Multiple Endpoints: Application to a Phase I Trial in Non-Small Cell Lung Cancer

Author

Listed:
  • Bethany Jablonski Horton

    (Division of Translational Research and Applied Statistics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22904, USA)

  • Nolan A. Wages

    (Division of Translational Research and Applied Statistics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22904, USA)

  • Ryan D. Gentzler

    (Division of Hematology/Oncology, University of Virginia Cancer Center, Charlottesville, VA 22904, USA)

Abstract

Immunotherapy and chemotherapy combinations have proven to be a safe and efficacious treatment approach in multiple settings. However, it is not clear whether approved doses of chemotherapy developed to achieve a maximum tolerated dose are the ideal dose when combining cytotoxic chemotherapy with immunotherapy to induce immune responses. This trial of a modulated dose chemotherapy and Pembrolizumab, with or without a second immunomodulatory agent, uses a Bayesian design to select the optimal treatment combination by balancing both safety and efficacy of the chemotherapy and immunotherapy agents within each of two cohorts. The simulation study provides evidence that the proposed Bayesian design successfully addresses the primary study aim to identify the optimal dose combination for each of the two independent patient cohorts. This conclusion is supported by the high percentage of simulated trials which select a treatment combination that is both safe and highly efficacious. The proposed trial was funded and was being finalized when the sponsoring company decided not to proceed due to negative findings in another patient population. The proposed trial design will continue to be relevant as multiple chemotherapy and immunotherapy combinations become the standard of care and future research will require evaluating the appropriate doses of various components of multiple drug regimens.

Suggested Citation

  • Bethany Jablonski Horton & Nolan A. Wages & Ryan D. Gentzler, 2021. "Bayesian Design for Identifying Cohort-Specific Optimal Dose Combinations Based on Multiple Endpoints: Application to a Phase I Trial in Non-Small Cell Lung Cancer," IJERPH, MDPI, vol. 18(21), pages 1-10, October.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:21:p:11452-:d:669097
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    References listed on IDEAS

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    1. Nolan A. Wages & Mark R. Conaway & John O'Quigley, 2011. "Continual Reassessment Method for Partial Ordering," Biometrics, The International Biometric Society, vol. 67(4), pages 1555-1563, December.
    2. Alessandra Giovagnoli, 2021. "The Bayesian Design of Adaptive Clinical Trials," IJERPH, MDPI, vol. 18(2), pages 1-15, January.
    3. J. C. Naylor & A. F. M. Smith, 1982. "Applications of a Method for the Efficient Computation of Posterior Distributions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(3), pages 214-225, November.
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