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在重新随机化测试中的重复次数
Yilong Zhang1, Yujie Zhao2, Bingjun Wang2
1Reality Labs, Meta Platforms Inc., Menlo Park, California, USA.
Pharmaceutical statistics
|October 16, 2024
概括
再随机化测试为适应性随机化提供了有效的统计推断,但需要多次重复. 这项研究引入了一种适应性程序,以减少临床试验中的计算负担,使这些测试更实用.
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
- 统计推理 统计推理
背景情况:
- 相关的治疗分配和结果发生在适应性随机化中.
- 在这种情况下,重新随机化测试可以提供有效的统计推断.
- 临床试验中的组序列设计可能需要极小的显著性边界.
研究的目的:
- 调查重新随机化测试所需的重复次数.
- 解决许多重复的计算难度.
- 提出和评估一种适应性程序,以减少计算需求.
主要方法:
- 开发了一种适应性程序,以减少重复次数.
- 将拟议的程序与现有方法进行比较.
- 利用蒙特卡洛模拟来评估有限样本大小的性能.
主要成果:
- 拟议的适应性程序有效地减少了所需的重复次数.
- 模拟证明了该方法在有限的样本大小设置中的可行性.
- 确定了减少整体计算时间的策略.
结论:
- 适应程序为重新随机化测试提供了一个计算上可行的替代方案.
- 为在临床试验中有效实施这些测试提供了实际指导.
- 这些发现增强了适应性临床试验设计中的重新随机化试验的实用性.
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