Abstract

Advances in biomedicine have fueled the development of targeted agents in cancer therapy. Targeted therapies have shown to be more efficacious and less toxic than the conventional chemotherapies. Targeted therapies, however, do not work for all patients. One major challenge is to identify markers for predicting treatment efficacy. We have developed biomarker-based Bayesian adaptive designs to (1) identify prognostic and predictive markers for targeted agents, (2) test treatment efficacy, and (3) provide better treatments for patients enrolled in the trial. In contrast to the frequentist equal randomization designs, Bayesian adaptive randomization designs allow treating more patients with effective treatments, monitoring the trial more frequently to stop ineffective treatments early, and increasing efficiency while controlling type I and type II errors. Bayesian adaptive design can be more efficient, more ethical, and more flexible in the study conduct than standard design s. We have recently completed a biopsy-required, biomarker-driven lung cancer trial, BATTLE , for evaluating four targeted treatments. Lessons learned from the design, conduct, and analysis of this Bayesian adaptive design will be given.