Response-Adaptive Randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are typically used as a motivating application. In that context, patient allocation to treatments is determined by randomization probabilities that change based on the accrued response data in order to achieve experimental goals. RAR has received abundant theoretical attention from the biostatistical literature since the 1930's and has been the subject of numerous debates. In the last decade, it has received renewed consideration from the applied and methodological communities, driven by well-known practical examples and its widespread use in machine learning. Papers on the subject present different views on its usefulness, and these are not easy to reconcile. This work aims to address this gap by providing a unified, broad and fresh review of methodological and practical issues to consider when debating the use of RAR in clinical trials.

翻译：反应-适应性随机化(RAR)是更广泛的数据依赖抽样算法类别的一部分,临床试验通常用作激励性应用,在这方面,病人对治疗的分配是由随机化概率决定的,这种概率是根据累积答复数据的变化,以实现实验目标;自1930年代以来,生物统计文献对RAR进行了大量的理论关注,并成为许多辩论的主题;在过去十年里,在众所周知的实际实例及其在机器学习中的广泛应用的推动下,应用和方法界对RAR的重新进行了考虑;关于该主题的论文就RAR的效用提出了不同的观点,这些观点不容易调和;这项工作的目的是通过统一、广泛和新鲜地审查方法和实际问题,在临床试验中辩论RAR的使用时加以考虑,从而弥补这一差距。