在具有连续终点的平台试验中的最佳分配策略
Marta Bofill Roig1, Ekkehard Glimm2, Tobias Mielke3
1Section for Medical Statistics, Center for Medical Data Science, Medical University of Vienna, Wien, Austria.
Statistical methods in medical research
|March 20, 2024
概括
在平台试验中确定最佳的患者分配是复杂的,因为治疗手臂的数量不断变化. 这项研究为具有共享控制的平台试验获得了新的最佳分配率,改进了传统方法.
科学领域:
- 临床试验方法论 临床试验方法论
- 生物统计学 生物统计学
- 药物开发 药物开发
背景情况:
- 平台试验可以同时比较多种干预措施与共同的控制.
- 在平台试验中的实验臂数量可以随着时间的推移动态变化.
- 在这些试验中,最佳的患者分配是具有挑战性的,因为手臂数量和分析策略的时间变化.
研究的目的:
- 在具有共享控制的平台试验中获得最佳的治疗分配率.
- 为了解决因时间变化的臂数和不同的分析策略而产生的复杂性.
- 在固定的总样本大小下,尽量减少效果估计器的最大偏差.
主要方法:
- 对具有共享控制的平台试验的最佳分配率的推导.
- 使用分层估计和基于回归的测试程序来调整时间趋势.
- 仅考虑与并发控制以及与并发和非并发控制的分析.
- 尽量减少效果估计器的最大偏差作为目标函数.
主要成果:
- 最佳的分配率取决于武器进入试验的时间.
- 导出的最佳解决方案通常不同于在经典的多臂试验中使用的N分配规则的平方根.
- 案例研究说明了最佳分配,并与标准分配相比,评估了功率和1型错误率.
结论:
- 该研究为动态平台试验提供了最佳的分配率,超越了传统的固定臂试验方法.
- 最佳的分配策略对于高效的平台试验设计和可靠的结果至关重要.
- 这些发现为设计和分析复杂的适应性临床试验提供了实际指导.
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