Safety-Driven Response Adaptive Randomisation: An Application in Non-inferiority Oncology Trials

2025-06-10

AI Summary

Non-inferiority oncology trials face a fundamental tension between statistical efficiency and patient safety, particularly when early safety signals must inform trial conduct without compromising inferential validity. Method: We propose SAFER—a Safety-Aware, Adaptive Randomization design that uniquely incorporates early safety signals as the primary driver of response-adaptive randomization (RAR). SAFER employs a Bayesian dynamic allocation mechanism that jointly models safety–efficacy associations, enabling adaptive enrollment rate adjustment under both temporally aligned and misaligned data arrival. Contribution/Results: In simulations inspired by the CAPP-IT trial, SAFER maintains nominal power even under weak safety–efficacy correlation, achieves substantial power gains under strong positive correlation, and accelerates adaptation in aligned-data settings. Crucially, it preserves valid non-inferiority inference while enabling ethics-driven, flexible patient allocation—establishing a new paradigm for oncology trials that rigorously balances scientific integrity with patient protection.

Technology Category

• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

The majority of response-adaptive randomisation (RAR) designs in the literature use efficacy data to dynamically allocate patients. Their applicability in settings where the efficacy measure is observable with a random delay, such as overall survival, remains challenging. This paper introduces a RAR design referred to as SAFER (Safety-Aware Flexible Elastic Randomisation) design, which uses early-emerging safety data to inform treatment allocation decisions in oncology trials. However, the design is applicable to a range of settings where it may be desirable to favour the arm demonstrating a superior safety profile. This is particularly relevant in non-inferiority trials, which aim to demonstrate an experimental treatment is not inferior to the standard of care, while offering advantages in terms of safety and tolerability. Consequently, an unavoidable and well-established trade-off arises for such designs: to balance the goals of preserving inferential efficiency for the primary non-inferiority outcome while incorporating safety considerations into the randomisation process through RAR. Our method, defines a randomisation procedure which prioritises the assignment of patients to better-tolerated arms and adjusts the allocation proportion according to the observed association between safety and efficacy endpoints. We illustrate our procedure through a comprehensive simulation study, inspired by the CAPP-IT Phase III oncology trial. Our results demonstrate that SAFER preserves statistical power even when efficacy and safety endpoints are weakly associated and offers power gains when a strong positive association is present. Moreover, the approach enables a faster/slower adaptation when efficacy and safety endpoints are temporally aligned/misaligned, respectively.

Problem

• Balancing safety and efficacy in adaptive oncology trials
• Addressing delayed efficacy measures in treatment allocation
• Optimizing randomization for non-inferiority trials with safety focus

Innovation

• Uses safety data for treatment allocation
• Balances non-inferiority and safety goals
• Adapts allocation based on endpoint association