一个预先计划的多阶段平台试验,用于发现多种高级处理方法,控制FWER和电力
Peter Greenstreet1,2, Thomas Jaki3,4, Alun Bedding5
1Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.
Biometrical journal. Biometrische Zeitschrift
|December 23, 2024
概括
本研究提出了一个多阶段的平台试验设计,用于添加新的治疗方法,同时控制错误率. 它详细说明了样本大小计算,显示平台试验在控制家庭智能错误率时可能并不总是比单独试验提供优势.
科学领域:
- 临床试验 临床试验
- 生物统计学 生物统计学
- 统计学方法论 统计学方法论
背景情况:
- 平台试验在增加/删除治疗臂和早期停止方面提供了灵活性.
- 控制错误率,特别是家庭智能错误率 (FWER),在复杂的试验设计中至关重要.
- 当新的臂被连续添加时,现有的方法可能无法充分解决样本大小和错误控制问题.
研究的目的:
- 为平台试验引入预先计划的多阶段设计,允许顺序添加治疗臂.
- 开发方法,在整个试验中保持家庭智能的错误率控制.
- 确定样本大小要求,以实现所需的统计功率,即使在确定了优质处理后.
主要方法:
- 为平台试验提出一个多阶段的统计设计.
- 为预期的样本大小推导计算.
- 使用激励性的临床试验示例评估设计,并比较配置.
主要成果:
- 拟议的设计允许顺序添加处理臂,同时保持家庭智能的错误率控制.
- 为各种场景提供样本大小计算,包括在发现优势后继续测试.
- 对比表明,当需要FWER控制时,平台试验可能并不总是会在多个单独的试验中产生样本大小优势.
结论:
- 开发的多阶段设计为灵活的平台试验提供了一个结构化的方法,具有强大的错误控制.
- 了解样本大小的影响是有效实施平台试验的关键.
- 这些发现强调需要仔细考虑平台试验优势与基于特定统计目标的传统设计,如FWER控制.
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