Biomarker-Guided Adaptive Enrichment Design with Threshold Detection for Clinical Trials with Time-to-Event Outcome

TL;DR

This paper introduces a novel adaptive enrichment design for clinical trials with time-to-event outcomes, utilizing RMST instead of hazard ratios to improve interpretability and flexibility in biomarker-guided patient selection.

Contribution

It develops a two-stage biomarker-guided adaptive RMST design with threshold detection, including methods for optimal biomarker threshold identification and statistical analysis.

Findings

Effective identification of optimal biomarker thresholds

Improved treatment effect estimation in biomarker-positive subgroup

Enhanced trial efficiency demonstrated through simulations

Abstract

Biomarker-guided designs are increasingly used to evaluate personalized treatments based on patients' biomarker status in Phase II and III clinical trials. With adaptive enrichment, these designs can improve the efficiency of evaluating the treatment effect in biomarker-positive patients by increasing their proportion in the randomized trial. While time-to-event outcomes are often used as the primary endpoint to measure treatment effects for a new therapy in severe diseases like cancer and cardiovascular diseases, there is limited research on biomarker-guided adaptive enrichment trials in this context. Such trials almost always adopt hazard ratio methods for statistical measurement of treatment effects. In contrast, restricted mean survival time (RMST) has gained popularity for analyzing time-to-event outcomes because it offers more straightforward interpretations of treatment effects and does not require the proportional hazard assumption. This paper proposes a two-stage biomarker-guided adaptive RMST design with threshold detection and patient enrichment. We develop sophisticated methods for identifying the optimal biomarker threshold, treatment effect estimators in the biomarker-positive subgroup, and approaches for type I error rate, power analysis, and sample size calculation. We present a numerical example of re-designing an oncology trial. An extensive simulation study is conducted to evaluate the performance of the proposed design.