Statistics in Biosciences

, Volume 8, Issue 1, pp 99–128 | Cite as

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

  • Xuemin Gu
  • Nan Chen
  • Caimiao Wei
  • Suyu Liu
  • Vassiliki A. Papadimitrakopoulou
  • Roy S. Herbst
  • J. Jack LeeEmail author


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.


Adaptive design Outcome-adaptive randomization Bayesian Lasso Predictive and prognostic biomarkers Personalized medicine Targeted therapy Variable selection 



The authors thank Ms. LeeAnn Chastain for editorial assistance. The work was supported in part by grants CA016672 and CA155196 from the National Cancer Institute. The clinical trial was supported in part by Merck Research Laboratories and Bayer HealthCare. We also would like to thank two anonymous reviewers, the associate editor, and the editor for their thorough review and constructive critiques. Our manuscript has been improved by providing answers in addressing these critics.

Conflict of interest

The authors did not have any conflict of interest related to this work to disclose.

Supplementary material

12561_2014_9124_MOESM1_ESM.docx (48 kb)
Supplementary material 1 (docx 48 KB)


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Copyright information

© International Chinese Statistical Association 2014

Authors and Affiliations

  • Xuemin Gu
    • 1
    • 2
  • Nan Chen
    • 1
  • Caimiao Wei
    • 1
  • Suyu Liu
    • 1
  • Vassiliki A. Papadimitrakopoulou
    • 3
  • Roy S. Herbst
    • 4
  • J. Jack Lee
    • 1
    Email author
  1. 1.Department of BiostatisticsThe University of Texas MD Anderson Cancer CenterHoustonUSA
  2. 2.PrincetonUSA
  3. 3.Department of Thoracic, Head & Neck Medical OncologyThe University of Texas MD Anderson Cancer CenterHoustonUSA
  4. 4.Department of Internal MedicineYale School of MedicineNew HavenUSA

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