The majority of response-adaptive randomisation (RAR) designs in the literature rely on efficacy data to guide dynamic patient allocation. However, their applicability becomes limited in settings where efficacy outcomes, such as survival, are observed with a random delay. To address this limitation, we introduce SAFER, a novel RAR design that leverages early-emerging safety data to inform treatment allocation decisions, particularly in oncology trials. The design is broadly applicable to contexts where prioritizing the arm with a superior safety is desirable. This is especially relevant in non-inferiority trials, to demonstrate that an experimental treatment is not inferior to the standard of care, while potentially offering improved tolerability. In such trials, an unavoidable trade-off arises: maintaining statistical efficiency for the efficacy hypothesis while integrating safety-driven adaptations through RAR. The SAFER design addresses this trade-off by dynamically adjusting the allocation proportion based on the observed association between safety and efficacy endpoints. We illustrate the performance of SAFER through a simulation study inspired by the CAPP-IT Phase III oncology trial. Results show that SAFER preserves statistical power, reduces the adverse event rate, and offers flexible adaptation speed depending on the temporal alignment of the endpoints.

翻译：文献中大多数应答自适应随机化设计依赖疗效数据指导动态患者分配。然而，当疗效结局（如生存期）存在随机延迟观测时，其适用性受到限制。为解决这一局限，我们提出了SAFER——一种利用早期安全性数据指导治疗分配决策的新型RAR设计，特别适用于肿瘤学试验。该设计广泛适用于优先考虑安全性更优治疗组的场景，这在非劣效性试验中尤为重要，旨在证明实验性治疗不劣于标准疗法，同时可能提供更好的耐受性。此类试验中不可避免地面临权衡：在保持疗效假设统计效能的同时，通过RAR整合安全性驱动的适应性调整。SAFER设计通过基于观测到的安全性与疗效终点关联动态调整分配比例，有效解决了这一权衡问题。我们通过受CAPP-IT III期肿瘤试验启发的模拟研究展示了SAFER的性能。结果表明，SAFER在保持统计功效的同时降低了不良事件发生率，并能根据终点的时间对应关系提供灵活的调整速度。