临床试验中的响应自适应随机化:当前实践,差距和未来方向
Isabelle Wilson1, Steven Julious1, Christina Yap2
1SCHARR, The University of Sheffield, UK.
Statistical methods in medical research
|June 18, 2025
概括
响应自适应随机化 (RAR) 试验在优化治疗分配和减少样本大小方面表现有前途,特别是在瘤学中. 尽管有好处,但复杂性和报告差距阻碍了这种自适应设计方法的更广泛采用.
科学领域:
- 临床试验设计 临床试验设计
- 生物统计学 生物统计学
- 医学研究方法学 医学研究方法学
背景情况:
- 适应性设计 (ADs) 允许临床试验根据中间数据修改参数.
- 响应自适应随机化 (RAR) 是一种AD,它调整治疗分配以支持更有效的治疗方法.
- 尽管有优势,但与其他AD相比,RAR的采用是有限的.
研究的目的:
- 检查响应自适应随机化 (RAR) 试验的应用,结果和益处.
- 识别与RAR相关的报告缺口,统计工具和问题.
- 在RAR实施中突出有效的实践和未来的研究需求.
主要方法:
- 对RAR试验进行叙述和描述性分析,以比较疗效,有效性或安全性目标 (I/II期或更高).
- 搜索了统计文献,试验注册表和研究人员知识,截至2023年10月 (结果截至2024年2月).
- 选了652篇文章/试验,确定了65项计划中的RAR试验 (11个平台试验).
主要成果:
- 大多数RAR试验是瘤学 (25%) 和药物治疗 (80%),主要是II期 (62%) 和多臂 (63%),使用贝叶斯方法 (85%).
- 71%的试验缺乏明确的统计实施细节,51%的试验限制了分配,88%的试验包括燃烧期.
- 有结果的试验显示,平均样本大小减少了22%,没有过度分配给无效的武器,尽管超过50%的分配变化报告不充分.
结论:
- 通过改善治疗分配和节约资源,RAR已在败血症,COVID-19和癌症中显示出好处.
- 由于RAR的复杂性,成本以及对强大的模拟的需求,RAR的更广泛采用受到阻碍.
- 加强统计工具和解决报告缺陷对于促进临床研究中的RAR至关重要.
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