Multi-arm trials are gaining interest in practice given the statistical and logistical advantages they can offer. The standard approach uses a fixed allocation ratio, but there is a call for making it adaptive and skewing the allocation of patients towards better-performing arms. However, it is well-known that these approaches might suffer from lower statistical power. We present a response-adaptive design for continuous endpoints which explicitly allows to control the trade-off between the number of patients allocated to the "optimal" arm and the statistical power. Such a balance is achieved through the calibration of a tuning parameter, and we explore robust procedures to select it. The proposed criterion is based on a context-dependent information measure which gives greater weight to treatment arms with characteristics close to a pre-specified clinical target. We establish conditions under which the procedure consistently selects the target arm and derive the corresponding limiting allocation ratios. We also introduce a simulation-based hypothesis testing procedure which focuses on selecting the target arm and discuss strategies to effectively control the type-I error rate. The practical implementation of the proposed criterion and its potential advantage over currently used alternatives are illustrated in the context of early Phase IIa proof-of-concept oncology trials.

翻译：多臂试验因其在统计和操作上的优势，在实践中日益受到关注。标准方法采用固定的分配比例，但学界呼吁将其设计为自适应形式，使患者分配向表现更优的试验组倾斜。然而，这类方法可能伴随统计功效降低的问题已广为人知。本文提出一种针对连续终点的响应自适应设计，该设计能明确控制分配至"最优"试验组的患者数量与统计功效之间的权衡。这种平衡通过调节一个可调参数实现，我们探索了稳健的参数选择流程。所提出的准则基于一种情境依赖的信息度量，该度量对特征接近预设临床目标的治疗组赋予更高权重。我们建立了该流程能一致选择目标试验组的条件，并推导出相应的极限分配比例。同时，我们提出一种基于模拟的假设检验流程，其聚焦于目标试验组的选择，并讨论了有效控制第一类错误率的策略。通过早期IIa期概念验证肿瘤学试验的实例，我们展示了所提准则的实际应用及其相对于现有方法的潜在优势。