Optimal Biomarker-Guided Design for Targeted Therapy with Imperfectly Measured Biomarkers

Targeted therapy revolutionizes the way physicians treat cancer and other diseases, enabling them to adaptively select individualized treatment according to the patient’s biomarker profile. The implementation of targeted therapy requires that the biomarkers are accurately measured, which may not always be feasible in practice. In this article, we propose two optimal biomarker-guided trial designs in which the biomarkers are subject to measurement errors. The first design focuses on a patient’s individual benefit and minimizes the treatment assignment error so that each patient has the highest probability of being assigned to the treatment that matches his/her true biomarker status. The second design focuses on the group benefit, which maximizes the overall response rate for all the patients enrolled in the trial. We develop a likelihood ratio test to evaluate the subgroup treatment effects at the end of the trial. Simulation studies show that the proposed optimal designs achieve our design goal and obtain desirable operating characteristics.