LAUSR

The challenges and potential benefits of incorporating biomarkers into clinical trial designs have been increasingly discussed, in particular to develop new agents for immune-oncology or targeted cancer therapies. To more accurately identify a sensitive subpopulation of patients, in many cases, a larger sample size-and consequently higher development costs and a longer study period-might be required. This article discusses a biomarker-based Bayesian (BM-Bay) randomized clinical trial design that incorporates a predictive biomarker measured on a continuous scale with pre-determined cutoff points or a graded scale to define multiple patient subpopulations. We consider designing interim analyses with suitable decision criteria to achieve correct and efficient identification of a target patient population for developing a new treatment. The proposed decision criteria allow not only the take-in of sensitive subpopulations but also the ruling-out of insensitive ones on the basis of the efficacy evaluation of a time-to-event outcome. Extensive simulation studies are conducted to evaluate the operating characteristics of the proposed method, including the probability of correct identification of the desired subpopulation and the expected number of patients, under a wide range of clinical scenarios. For illustration purposes, we apply the proposed method to design a randomized phase II immune-oncology clinical trial.