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

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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.

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Acknowledgments

The research of YJ and PM is partly supported by NIH R01 CA132897. PM was also partly supported by NIH R01CA157458. This research was supported in part by NIH through resources provided by the Computation Institute and the Biological Sciences Division of the University of Chicago and Argonne National Laboratory, under Grant S10 RR029030-01. We specifically acknowledge the assistance of Lorenzo Pesce (U of Chicago) and Yitan Zhu (NorthShore University HealthSystem).

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Authors and Affiliations

  1. Division of Statistics and Scientific Computing, The University of Texas at Austin, Austin, TX, USA

    Yanxun Xu

  2. Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA

    Lorenzo Trippa

  3. Department of Mathematics, The University of Texas at Austin, Austin, TX, USA

    Peter Müller

  4. Center for Clinical and Research Informatics, NorthShore University Health System, Evanston, IL, USA

    Yuan Ji

  5. Department of Health Studies, The University of Chicago, Chicago, IL, USA

    Yuan Ji

Authors
  1. Yanxun Xu
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  2. Lorenzo Trippa
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  3. Peter Müller
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  4. Yuan Ji
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Correspondence to Yuan Ji.

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Xu, Y., Trippa, L., Müller, P. et al. Subgroup-Based Adaptive (SUBA) Designs for Multi-arm Biomarker Trials. Stat Biosci 8, 159–180 (2016). https://doi.org/10.1007/s12561-014-9117-1

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  • DOI: https://doi.org/10.1007/s12561-014-9117-1

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