Adaptive Biomarker-Based Design for Early Phase Clinical Trials

Abstract

Identifying and quantifying predictive biomarkers is a critical issue of Precision Medicine approaches and patient-centric clinical development strategies. Early phase adaptive designs can improve trial efficiency by allowing for adaptations during the course of the trial. In this work, we are interested in adaptations based on interim analysis permitting a refinement of the existing study population according to their predictive biomarkers. At an early stage, the goal is not to precisely define the target population, but to not miss an efficacy signal that might be limited to a biomarker subgroup. In this work, we propose a one-arm two-stage early phase biomarker-guided design in the setting of an oncology trial where at the time of the interim analysis, several decisions can be made regarding stopping the entire trial early or continuing to recruit patients from the full or a selected patient population. Via simulations, we show that, although the sample size is limited, the proposed design leads to better decision-making compared to a classical design that does not consider an enrichment expansion.

Keywords: continuous biomarker; early phase; personalized medicine; predictive.

FIGURE 1

Panel (a): schematic of the decision rules at the IA for the original design. Panel (b): schematic of the decision rules at the IA for the biomarker‐guided design.

FIGURE 2

Top Panel: decisions at the IA for the proposed design. Middle Panel: overall decisions at the end of the trial for both designs regardless of the population and the timing of the analysis for all scenarios. Bottom Panel: conditional decisions at the end of the trial for the proposed design with full or sub‐population or regardless of the population conditional on continuing to the second stage for all scenarios.