Biomarker-Guided Multi-Stage Trials with Threshold Detection and Patient Enrichment with Information Borrowing from Historical Controls

Oregon Convention Center 

It is common to have a treatment-predictive continuous biomarker with unknown cutoff points so that the biomarker-positive subgroup that benefits most from the new treatment cannot be determined at the time of designing the biomarker-guided clinical trial. Biomarker-guided multi-stage design is often preferred as it allows adaptively identifying biomarker threshold that defines biomarker-positive subgroup and flexible patient enrichment in the late stage of the ongoing trial. In this talk, we will focus on designing biomarker-guided multi-stage adaptive trials with threshold detection, patient enrichment, and possible information borrowing from historical controls. We will discuss algorithms that adaptively identify optimal biomarker threshold to define the patient subgroup that benefits most from the new treatment, decision-making for patient enrichment for better efficiency, and information borrowing for threshold detection and treatment effect inference from historical controls that are subject to measured or unmeasured baseline confounders. The operating characteristics of the proposed design with competing designs are evaluated by extensive simulation.