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Subgroup-Based Adaptive (SUBA) Designs for Multi-arm Biomarker Trials

Author

Listed:
  • Yanxun Xu

    (The University of Texas at Austin)

  • Lorenzo Trippa

    (Harvard School of Public Health)

  • Peter Müller

    (The University of Texas at Austin)

  • Yuan Ji

    (NorthShore University Health System
    The University of Chicago)

Abstract

Targeted therapies based on biomarker profiling are becoming a mainstream direction of cancer research and treatment. Depending on the expression of specific prognostic biomarkers, targeted therapies assign different cancer drugs to subgroups of patients even if they are diagnosed with the same type of cancer by traditional means, such as tumor location. For example, Herceptin is only indicated for the subgroup of patients with HER2+ breast cancer, but not other types of breast cancer. However, subgroups like HER2+ breast cancer with effective targeted therapies are rare, and most cancer drugs are still being applied to large patient populations that include many patients who might not respond or benefit. Also, the response to targeted agents in humans is usually unpredictable. To address these issues, we propose subgroup-based adaptive (SUBA), designs that simultaneously search for prognostic subgroups and allocate patients adaptively to the best subgroup-specific treatments throughout the course of the trial. The main features of SUBA include the continuous reclassification of patient subgroups based on a random partition model and the adaptive allocation of patients to the best treatment arm based on posterior predictive probabilities. We compare the SUBA design with three alternative designs including equal randomization, outcome-adaptive randomization, and a design based on a probit regression. In simulation studies, we find that SUBA compares favorably against the alternatives.

Suggested Citation

  • Yanxun Xu & Lorenzo Trippa & Peter Müller & Yuan Ji, 2016. "Subgroup-Based Adaptive (SUBA) Designs for Multi-arm Biomarker Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(1), pages 159-180, June.
    • Handle: RePEc:spr:stabio:v:8:y:2016:i:1:d:10.1007_s12561-014-9117-1
    DOI: 10.1007/s12561-014-9117-1
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    References listed on IDEAS

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    1. Guosheng Yin & Nan Chen & J. Jack Lee, 2012. "Phase II trial design with Bayesian adaptive randomization and predictive probability," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(2), pages 219-235, March.
    2. Baladandayuthapani, Veerabhadran & Ji, Yuan & Talluri, Rajesh & Nieto-Barajas, Luis E. & Morris, Jeffrey S., 2010. "Bayesian Random Segmentation Models to Identify Shared Copy Number Aberrations for Array CGH Data," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1358-1375.
    3. Riten Mitra & Peter Müller & Shoudan Liang & Lu Yue & Yuan Ji, 2013. "A Bayesian Graphical Model for ChIP-Seq Data on Histone Modifications," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 69-80, March.
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    Cited by:

    1. Juhee Lee & Peter F. Thall & Pavlos Msaouel, 2023. "Bayesian treatment screening and selection using subgroup‐specific utilities of response and toxicity," Biometrics, The International Biometric Society, vol. 79(3), pages 2458-2473, September.

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