Lifetime Data Analysis

, Volume 17, Issue 1, pp 156–174 | Cite as

Bayesian phase II adaptive randomization by jointly modeling time-to-event efficacy and binary toxicity

  • Xiudong Lei
  • Ying Yuan
  • Guosheng YinEmail author


In oncology, toxicity is typically observable shortly after a chemotherapy treatment, whereas efficacy, often characterized by tumor shrinkage, is observable after a relatively long period of time. In a phase II clinical trial design, we propose a Bayesian adaptive randomization procedure that accounts for both efficacy and toxicity outcomes. We model efficacy as a time-to-event endpoint and toxicity as a binary endpoint, sharing common random effects in order to induce dependence between the bivariate outcomes. More generally, we allow the randomization probability to depend on patients’ specific covariates, such as prognostic factors. Early stopping boundaries are constructed for toxicity and futility, and a superior treatment arm is recommended at the end of the trial. Following the setup of a recent renal cancer clinical trial at M. D. Anderson Cancer Center, we conduct extensive simulation studies under various scenarios to investigate the performance of the proposed method, and compare it with available Bayesian adaptive randomization procedures.


Adaptive randomization Efficacy Phase II trial Survival analysis Time-to-event endpoint Toxicity 


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Biostatistics, Unit 1411The University of Texas M. D. Anderson Cancer CenterHoustonUSA
  2. 2.Department of Statistics and Actuarial ScienceUniversity of Hong KongHong KongChina

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