Adaptive Biomarker-Based Design for Early Phase Clinical Trials

Identifying and quantifying predictive biomarkers is a critical issue of Precision Medicine approaches and patient-centric clinical development strategies. Early phase adaptive designs can improve trial efficiency by allowing for adaptations during the course of the trial. In this work, we are interested in adaptations based on interim analysis permitting a refinement of the existing study population according to their predictive biomarkers. At an early stage, the goal is not to precisely define the target population, but to not miss an efficacy signal that might be limited to a biomarker subgroup. In this work, we propose a one-arm two-stage early phase biomarker-guided design in the setting of an oncology trial where at the time of the interim analysis, several decisions can be made regarding stopping the entire trial early or continuing to recruit patients from the full or a selected patient population. Via simulations, we show that, although the sample size is limited, the proposed design leads to better decision-making compared to a classical design that does not consider an enrichment expansion.