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Bayesian Two-Stage Biomarker-Based Adaptive Design for Targeted Therapy Development

Author

Listed:
  • Xuemin Gu

    (The University of Texas MD Anderson Cancer Center)

  • Nan Chen

    (The University of Texas MD Anderson Cancer Center)

  • Caimiao Wei

    (The University of Texas MD Anderson Cancer Center)

  • Suyu Liu

    (The University of Texas MD Anderson Cancer Center)

  • Vassiliki A. Papadimitrakopoulou

    (The University of Texas MD Anderson Cancer Center)

  • Roy S. Herbst

    (Yale School of Medicine)

  • J. Jack Lee

    (The University of Texas MD Anderson Cancer Center)

Abstract

We propose a Bayesian two-stage biomarker-based adaptive randomization (AR) design for the development of targeted agents. The design has three main goals: (1) to test the treatment efficacy, (2) to identify prognostic and predictive markers for the targeted agents, and (3) to provide better treatment for patients enrolled in the trial. To treat patients better, both stages are guided by the Bayesian AR based on the individual patient’s biomarker profiles. The AR in the first stage is based on a known marker. A Go/No-Go decision can be made in the first stage by testing the overall treatment effects. If a Go decision is made at the end of the first stage, a two-step Bayesian lasso strategy will be implemented to select additional prognostic or predictive biomarkers to refine the AR in the second stage. We use simulations to demonstrate the good operating characteristics of the design, including the control of per-comparison type I and type II errors, high probability in selecting important markers, and treating more patients with more effective treatments. Bayesian adaptive designs allow for continuous learning. The designs are particularly suitable for the development of multiple targeted agents in the quest of personalized medicine. By estimating treatment effects and identifying relevant biomarkers, the information acquired from the interim data can be used to guide the choice of treatment for each individual patient enrolled in the trial in real time to achieve a better outcome. The design is being implemented in the BATTLE-2 trial in lung cancer at the MD Anderson Cancer Center.

Suggested Citation

  • Xuemin Gu & Nan Chen & Caimiao Wei & Suyu Liu & Vassiliki A. Papadimitrakopoulou & Roy S. Herbst & J. Jack Lee, 2016. "Bayesian Two-Stage Biomarker-Based Adaptive Design for Targeted Therapy Development," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(1), pages 99-128, June.
    • Handle: RePEc:spr:stabio:v:8:y:2016:i:1:d:10.1007_s12561-014-9124-2
    DOI: 10.1007/s12561-014-9124-2
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    References listed on IDEAS

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    1. Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
    2. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    3. Lukas Meier & Sara Van De Geer & Peter Bühlmann, 2008. "The group lasso for logistic regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 53-71, February.
    4. Hu, Feifang & Rosenberger, William F., 2003. "Optimality, Variability, Power: Evaluating Response-Adaptive Randomization Procedures for Treatment Comparisons," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 671-678, January.
    5. DiMasi, Joseph A. & Hansen, Ronald W. & Grabowski, Henry G., 2003. "The price of innovation: new estimates of drug development costs," Journal of Health Economics, Elsevier, vol. 22(2), pages 151-185, March.
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