贝叶斯响应-适应性随机化对集群随机对照试验的贝叶斯响应-适应性随机化
Yunyi Liu1, Maile Young Karris2, Sonia Jain1
1Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, California, USA.
Statistics in medicine
|January 22, 2026
概括
这项研究介绍了贝叶斯适应性随机化方法,用于集群随机试验. 这种方法有效地将更多的群体分配给有效的治疗方法,改善试验伦理和资源使用.
科学领域:
- 生物统计学 生物统计学
- 临床试验方法论 临床试验方法论
- 公共卫生研究 公共卫生研究
背景情况:
- 集群随机对照试验 (CRCT) 在个人随机化不切实际或干预是以小组为基础时至关重要.
- 标准CRCT随机化可能是低效的,导致资源紧张和道德问题,如果被试被分配到低于最佳的手臂.
- 适应性随机化为优化CRCT治疗分配提供了一个潜在的解决方案.
研究的目的:
- 提出一个新的贝叶斯响应适应性随机化设计用于使用普森采样的CRCT.
- 根据后面的概率,纳入早期停止规则的有效性和徒劳性.
- 评估性能,并将拟议的自适应设计与标准CRCT设计进行比较.
主要方法:
- 一个贝叶斯响应适应性随机化设计,采用普森采样和马尔科夫链蒙特卡洛 (MCMC) 采样.
- 根据治疗效应的中间后部分布,对治疗臂进行集群的动态分配.
- 实施早期停止规则的有效性和徒劳性,使用预规定的后方概率值.
主要成果:
- 与模拟研究中的标准CRCT设计相比,拟议的自适应设计证明了提高效率和伦理考虑.
- 该方法优先将更多的集群分配给更有效的干预.
- 强大的统计能力和受控的假阳性率在各种设置中得到维持,包括不同的集群内相关系数,集群大小和效果大小.
结论:
- 使用普森抽样的贝叶斯响应适应性随机化为CRCT提供了一种高效和道德的方法.
- 这种自适应设计通过动态分配集群到优质治疗来优化资源配置.
- 该方法对改善集群随机试验的进行有希望,这是基于HIV行为试验的模拟证明的.
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