一个多臂多阶段平台设计,允许预先计划添加武器,同时仍然控制家庭智能的错误
Peter Greenstreet1,2, Thomas Jaki3,4, Alun Bedding5
1Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.
Statistics in medicine
|June 16, 2024
概括
本研究介绍了一种多阶段平台试验设计,用于控制新疗法添加时的错误率. 这种灵活的设计优化了样本大小和功率,特别有利于具有多个治疗臂的试验.
科学领域:
- 生物统计学 生物统计学
- 临床试验设计 临床试验设计
背景情况:
- 平台试验提供灵活性,允许在研究期间添加新疗法和停止现有疗法.
- 控制统计错误率在适应性试验设计中至关重要,特别是在平台试验中.
研究的目的:
- 提出一个多阶段的平台试验设计,使新治疗臂的添加,同时保持控制家庭智能错误率.
- 提供计算所需样本大小和分析样本大小分布以实现功率的方法.
主要方法:
- 为平台试验提出了一个多阶段的统计设计.
- 设计假设已知的数量和时间处理添加,没有时间趋势.
- 详细说明了计算所需功率的样本大小和分析样本大小分布的方法.
主要成果:
- 拟议的设计控制了预先计划增加的平台试验的家庭智能错误率.
- 提供样本大小计算,以实现所需的统计能力,专注于最不有利的配置.
- 与Bonferroni相比,在三臂试验中观察到适度的样本大小节省 (<1%),在更多的手臂中节省的更大.
结论:
- 提出的多阶段设计为灵活平台试验提供了统计学上合理的方法.
- 这种方法对于优化资源配置和在适应性临床研究中保持统计学严谨性是有价值的.
- 该设计表明,在复杂的多臂平台试验中,样本大小有很大的效率潜力.
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