发表机构
MRC Biostatistics Unit, University of Cambridge（剑桥大学医学研究委员会生物统计学单元）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文综述了16个开源软件包，评估其在响应自适应随机化（RAR）中的方法与实践支持，发现多数软件针对特定方法，通用能力有限，并指出开发灵活、实用且验证充分的软件对RAR临床推广至关重要。
AI 中文摘要
响应自适应随机化（RAR）在临床试验过程中，随着反应/结果数据的积累，修改治疗分配概率，旨在提高患者获益、统计效率或两者兼得。尽管方法论上取得了显著进展，但RAR在临床实践中的采用仍然有限，且支持其设计和实施的软件此前未被综述。我们识别出16个公开可用的开源软件包，它们实现了RAR方法，涵盖基于瓮的、目标分配的、贝叶斯的、基于马尔可夫决策过程（MDP）的以及剂量寻找方法，并从方法论和实践特征方面对其进行了评估。我们发现，尽管存在多个软件包，但大多数是针对特定方法的，只有少数提供更广泛的通用自适应试验设计和分析能力。对实践相关特征的显式支持，包括延迟和缺失的结果数据、反应率的时间趋势、灵活的操作特征评估以及平台或多臂多阶段试验设计，在所识别的软件中很少或不存在。这些发现表明，尽管存在多种用于探索RAR设计的工具，但方法论文献与实践中可用的软件之间仍存在差距。持续开发灵活、面向实践且经过验证的软件对于RAR在临床研究中的更广泛采用至关重要，我们指出了进一步工作的有趣领域。
英文摘要
Response-adaptive randomization (RAR) modifies treatment allocation probabilities during a clinical trial as response/outcome data accumulate, with the aim of improving patient benefit, statistical efficiency, or both. Despite substantial methodological development, adoption of RAR in clinical practice has remained limited, and the software available to support its design and implementation has not previously been reviewed. We identified 16 publicly available, open-source software packages implementing RAR methods, spanning urn-based, target-allocation, Bayesian, Markov decision process (MDP)-based, and dose-finding approaches, and evaluated them with respect to their methodological and practical characteristics. We found that while several software packages exist, most are method-specific, and only a small number provide broader, general-purpose adaptive-trial design and analysis capabilities. Explicit support for practically relevant features, including delayed and missing outcome data, temporal trends in response rates, flexible operating-characteristic evaluation, and platform or multi-arm multi-stage trial designs, is rare or absent across the identified software. These findings suggest that while a diverse set of tools exists for exploring RAR designs, gaps remain between the methodological literature and the software available to implement it in practice. Continued development of flexible, practically oriented, and validated software is important for the wider adoption of RAR in clinical research, and we highlight interesting areas of further work.
Commentsv2: Corrected some entries in Tables 2 and 3 following additional verification against package source code and documentation. Conclusions unchanged
