Bayesian adaptive trial design for a continuous biomarker with possibly nonlinear or nonmonotone prognostic or predictive effects

As diseases like cancer are increasingly understood on a molecular level, clinical trials are being designed to reveal or validate subpopulations in which an experimental therapy has enhanced benefit. Such biomarker-driven designs, particularly "adaptive enrichment" designs that initially enroll an unselected population and then allow for later restriction of accrual to "marker-positive" patients based on interim results, are increasingly popular. Many biomarkers of interest are naturally continuous, however, and most existing design approaches either require upfront dichotomization or force monotonicity through algorithmic searches for a single marker threshold, thereby excluding the possibility that the continuous biomarker has a nondisjoint and truly nonlinear or nonmonotone prognostic relationship with outcome or predictive relationship with treatment effect. To address this, we propose a novel trial design that leverages both the actual shapes of any continuous marker effects (both prognostic and predictive) and their corresponding posterior uncertainty in an adaptive decision-making framework. At interim analyses, this marker knowledge is updated and overall or marker-driven decisions are reached such as continuing enrollment to the next interim analysis or terminating early for efficacy or futility. Using simulations and patient-level data from a multi-center Children's Oncology Group trial in Acute Lymphoblastic Leukemia, we derive the operating characteristics of our design and compare its performance to a traditional approach that identifies and applies a dichotomizing marker threshold.