Bayesian Two-Stage Biomarker-Based Adaptive Design for Targeted Therapy Development
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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.
KeywordsAdaptive 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.
- 2.Herper M (2012) The truly staggering cost of inventing new drugs. Forbes Website. http://www.forbes.com/sites/matthewherper/2012/02/10/the-truly-staggering-cost-of-inventing-new-drugs/
- 3.CSDD Outlook (2009) Backgrounder: a methodology for counting cost for pharmaceutical R&D. Tufts Center for the study of Drug Development, BostonGoogle Scholar
- 8.Mandrekar SJ, Sargent DJ (2001) Design of clinical trials for biomarker research in oncology. Clin Invest 1(12):1629–1636Google Scholar
- 16.Silvestri G, Rivera M (2005) Targeted therapy for the treatment of advanced non-small cell lung cancer: a review of the epidermal growth factor receptor antagonists. Chest 183:29–42Google Scholar
- 23.Kim ES, Herbst RS, Wistuba II, Lee JJ, Blumenschein GR, Tsao A, Stewart DJ, Hicks ME, Erasmus J, Gupta S, Alden CM, Liu S, Tang X, Khuri FR, Tran HT, Johnson BE, Heymach JV, Mao L, Fossella F, Kies MS, Papadimitrakopoulou V, Davis SE, Lippman SM, Hong WK (2011) The BATTLE trial: Personalizing therapy for lung cancer. Cancer Discov 1(1):44–53CrossRefGoogle Scholar
- 24.Berry DA (2005) Introduction to Bayesian methods III: use and interpretation of Bayesian tools in design and analysis. Clin Trials 2:295–300; discussion 301–304, 364–378Google Scholar
- 30.Berry DA (2004) Bayesian statistics and the efficiency and ethics of clinical trials. Stat Sci 19:175–187Google Scholar
- 31.Korn EL, Freidlin B (2010) Outcome-adaptive randomization: is it useful? J Clin Oncol 21:100–120Google Scholar
- 32.Berry DA (2010) Adaptive clinical trials: the promise and the caution. J Clin Oncol 21:606–609Google Scholar
- 49.Wang S-J, Li M-C (2013) Impacts of predictive genomic classifier performance on subpopulation-specific treatment effects assessment. Stat Biosci. doi: 10.1007/s12561-013-9092-y