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Bayesian dose finding in oncology for drug combinations by copula regression

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  • Guosheng Yin
  • Ying Yuan

Abstract

Treating patients with a combination of agents is becoming commonplace in cancer clinical trials, with biochemical synergism often the primary focus. In a typical drug combination trial, the toxicity profile of each individual drug has already been thoroughly studied in single-agent trials, which naturally offers rich prior information. We propose a Bayesian adaptive design for dose finding that is based on a copula-type model to account for the synergistic effect of two or more drugs in combination. To search for the maximum tolerated dose combination, we continuously update the posterior estimates for the toxicity probabilities of the combined doses. By reordering the dose toxicities in the two-dimensional probability space, we adaptively assign each new cohort of patients to the most appropriate dose. Dose escalation, de-escalation or staying at the same doses is determined by comparing the posterior estimates of the probabilities of toxicity of combined doses and the prespecified toxicity target. We conduct extensive simulation studies to examine the operating characteristics of the design and illustrate the proposed method under various practical scenarios. Copyright (c) 2009 Royal Statistical Society.

Suggested Citation

  • Guosheng Yin & Ying Yuan, 2009. "Bayesian dose finding in oncology for drug combinations by copula regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(2), pages 211-224.
  • Handle: RePEc:bla:jorssc:v:58:y:2009:i:2:p:211-224
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    File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-9876.2009.00649.x
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    References listed on IDEAS

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    1. Xuelin Huang & Swati Biswas & Yasuhiro Oki & Jean-Pierre Issa & Donald A. Berry, 2007. "A Parallel Phase I/II Clinical Trial Design for Combination Therapies," Biometrics, The International Biometric Society, vol. 63(2), pages 429-436, June.
    2. Mauro Gasparini & Jeffrey Eisele, 2000. "A Curve-Free Method for Phase I Clinical Trials," Biometrics, The International Biometric Society, vol. 56(2), pages 609-615, June.
    3. Saurabh Mukhopadhyay, 2000. "Bayesian Nonparametric Inference on the Dose Level with Specified Response Rate," Biometrics, The International Biometric Society, vol. 56(1), pages 220-226, March.
    4. Kai Wang & Anastasia Ivanova, 2005. "Two-Dimensional Dose Finding in Discrete Dose Space," Biometrics, The International Biometric Society, vol. 61(1), pages 217-222, March.
    5. Peter F. Thall & Randall E. Millikan & Peter Mueller & Sang-Joon Lee, 2003. "Dose-Finding with Two Agents in Phase I Oncology Trials," Biometrics, The International Biometric Society, vol. 59(3), pages 487-496, September.
    6. Mario Stylianou & Nancy Flournoy, 2002. "Dose Finding Using the Biased Coin Up-and-Down Design and Isotonic Regression," Biometrics, The International Biometric Society, vol. 58(1), pages 171-177, March.
    7. Mark R. Conaway & Stephanie Dunbar & Shyamal D. Peddada, 2004. "Designs for Single- or Multiple-Agent Phase I Trials," Biometrics, The International Biometric Society, vol. 60(3), pages 661-669, September.
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    Cited by:

    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. Mauro Gasparini & Stuart Bailey & Beat Neuenschwander, 2010. "Bayesian dose finding in oncology for drug combinations by copula regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(3), pages 543-544.
    3. 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.

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