Bayesian Basket Trial Design Accounting for Multiple Cutoffs of an Ambiguous Biomarker

Basket trial designs enroll patients with different cancer types but the same genetic mutation or biomarker to evaluate the treatment effect of targeted therapy. However, the explicit biomarker sometimes may not be clearly identified. In this article, we propose a Bayesian basket trial design to account for multiple cutoffs of ambiguous biomarkers and select the optimal cutoff window to maximize the beneficial subpopulation. A two-stage design is proposed for the estimation. Second, we propose threshold calibration and sample size determination to facilitate the design. Extensive simulations are conducted to demonstrate the operating characteristics of the two estimation methods in terms of probability of correct selection of optimal cutoff window and probability of efficacy.