将实验治疗臂添加到正在进行的多臂多阶段平台试验中.
Thomas Burnett1, Franz König2, Thomas Jaki3,4
1Department of Mathematical Sciences, University of Bath, Bath, UK.
Statistics in medicine
|June 9, 2024
概括
多臂多阶段 (MAMS) 平台试验可以在研究期间添加新的治疗方法. 这种方法确保了统计有效性和错误率控制,在将新假设纳入正在进行的试验时.
科学领域:
- 临床试验方法论 临床试验方法论
- 统计推理 统计推理
- 生物统计学 生物统计学
背景情况:
- 多臂多阶段 (MAMS) 平台试验可以有效地比较多种治疗与共同的对照.
- 这些设计本质上允许进行多重性调整,这对于保持统计完整性至关重要,特别是在不同的剂量或给药途径的情况下.
- 为了实际和统计效率,在正在进行的试验中添加新疗法是可取的.
研究的目的:
- 展示一种方法,在正在进行的MAMS平台试验中添加新的治疗臂.
- 为了确保家庭智能错误率 (FWER) 仍然受到严格控制,尽管设计修改.
- 调整现有的统计程序以纳入未计划的假设.
主要方法:
- 使用条件错误方法将新假设整合到FWER控制的测试程序中.
- 应用该框架来为已经进行的MAMS试验添加治疗方法.
- 使用模拟来评估拟议程序的特征.
主要成果:
- 条件错误方法为添加假设到FWER控制的程序提供了一个有效的框架.
- 这种方法成功地允许将新疗法纳入正在进行的MAMS试验.
- 模拟显示了这种修改试验设计的可行性和特征.
结论:
- 条件错误方法是一种强大的统计工具,可以提高MAMS平台试验的灵活性.
- 它可以无地添加新的治疗方法,而不会影响整体的统计有效性.
- 这种方法支持自适应性临床试验设计,提高效率和响应性.
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