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Flexible Phase I–II Design for Partially Ordered Regimens with Application to Therapeutic Cancer Vaccines

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  • Nolan A. Wages

    (University of Virginia)

  • Craig L. Slingluff

    (University of Virginia)

Abstract

Existing methodology for the design of Phase I–II studies has been intended to search for the optimal regimen, based on a tradeoff between toxicity and efficacy, from a set of regimens comprised of doses of a new agent. The underlying assumptions guiding allocation are that the dose–toxicity curve is monotonically increasing, and that the dose–efficacy curve either plateaus or decreases beyond an intermediate dose. This article considers the problem of designing Phase I—II studies that violate these assumptions for both outcomes. The motivating application studies regimens that are not defined by doses of a new agent, but rather a peptide vaccine plus novel adjuvants for the treatment of melanoma. All doses of each adjuvant are fixed, and the regimens vary by the number and selection of adjuvants. This structure produces regimen–toxicity curves that are partially ordered, and regimen–efficacy curves that may deviate from a plateau or unimodal shape. Application of a Bayesian model-based design is described in determining the optimal biologic regimen, based on bivariate binary measures of toxicity and biologic activity. A simulation study of the design’s operating characteristics is conducted, and its versatility in handling other Phase I–II problems is discussed.

Suggested Citation

  • Nolan A. Wages & Craig L. Slingluff, 2020. "Flexible Phase I–II Design for Partially Ordered Regimens with Application to Therapeutic Cancer Vaccines," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(2), pages 104-123, July.
  • Handle: RePEc:spr:stabio:v:12:y:2020:i:2:d:10.1007_s12561-019-09245-3
    DOI: 10.1007/s12561-019-09245-3
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    References listed on IDEAS

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    1. Yin, Guosheng & Yuan, Ying, 2009. "Bayesian Model Averaging Continual Reassessment Method in Phase I Clinical Trials," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 954-968.
    2. Chunyan Cai & Ying Yuan & Yuan Ji, 2014. "A Bayesian dose finding design for oncology clinical trials of combinational biological agents," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 159-173, January.
    3. John O'Quigley & Michael D. Hughes & Terry Fenton, 2001. "Dose-Finding Designs for HIV Studies," Biometrics, The International Biometric Society, vol. 57(4), pages 1018-1029, December.
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    Cited by:

    1. Bo Huang & Naitee Ting, 2020. "Introduction to Special Issue on ‘Statistical Methods for Cancer Immunotherapy’," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(2), pages 79-82, July.

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