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A dose–schedule finding design for phase I–II clinical trials

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  • Beibei Guo
  • Yisheng Li
  • Ying Yuan

Abstract

type="main" xml:id="rssc12113-abs-0001"> Dose finding methods aiming at identifying an optimal dose of a treatment with a given schedule may be at a risk of misidentifying the best treatment for patients. We propose a phase I–II clinical trial design to find the optimal dose–schedule combination. We define schedule as the method and timing of administration of a given total dose in a treatment cycle. We propose a Bayesian dynamic model for the joint effects of dose and schedule. The model proposed allows us to borrow strength across dose–schedule combinations without making overly restrictive assumptions on the ordering pattern of the schedule effects. We develop a dose–schedule finding algorithm to allocate patients sequentially to a desirable dose–schedule combination, and to select an optimal combination at the end of the trial. We apply the proposed design to a phase I–II clinical trial of a γ-secretase inhibitor in patients with refractory metastatic or locally advanced solid tumours, and we examine the operating characteristics of the design through simulations.

Suggested Citation

  • Beibei Guo & Yisheng Li & Ying Yuan, 2016. "A dose–schedule finding design for phase I–II clinical trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(2), pages 259-272, February.
  • Handle: RePEc:bla:jorssc:v:65:y:2016:i:2:p:259-272
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    File URL: http://hdl.handle.net/10.1111/rssc.2016.65.issue-2
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    Cited by:

    1. Pavel Mozgunov & Thomas Jaki, 2020. "An information theoretic approach for selecting arms in clinical trials," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1223-1247, December.

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