Determining the cutpoint of a continuous predictive biomarker via Bayesian sensitive subpopulation finding

Treatment effect heterogeneity has often been observed in clinical settings, which can be explained, at least partially, by predictive biomarkers. Although a variety of continuous biomarkers are measured in clinical practice, using biomarkers to guide treatment decisions remains challenging. We consider specifying a statistically and clinically suitable cutpoint of a continuous biomarker by identifying a sensitive subpopulation. We use a Bayesian posterior probability to analyze a time-to-event outcome and biomarkers observed in a randomized clinical trial. The proposed method aims not only to control the false-positive rate under a desired level, thereby deriving a statistically suitable decision rule, but also to incorporate a minimum clinically important difference, thereby providing a clinically reasonable interpretation. Simulation studies are conducted to evaluate the operating characteristics of the proposed method under a wide range of clinical scenarios. For illustration, we apply the proposed method to data from a phase III randomized clinical trial involving patients with advanced breast cancer.