一个用于A/B测试的自动化平台试验框架
Wenru Zhou1, Miranda Kroehl2, Maxene Meier2
1Department of Biostatistics and Informatics University of Colorado, United States of America.
Contemporary clinical trials communications
|November 29, 2024
概括
本研究探讨了A/B测试的平台试验设计,比较停止规则以优化样本大小和早期停止概率. 这就是为什么我会这样做.
科学领域:
- 临床试验设计 临床试验设计
- 统计学方法论 统计学方法论
- 平台试验 平台试验
背景情况:
- 平台试验可实现高效的A / B测试,使用多个臂和临时监控.
- 调查影响样本大小和早期停止的因素对于试验效率至关重要.
研究的目的:
- 在平台试验中评估不同停车界限的性能.
- 根据模拟结果,为选择最佳试验设计提供指导.
主要方法:
- 模拟了12个不同效果大小和臂排序的场景.
- 检查了O'Brien Fleming (OBF) 和Pocock的停止边界,以及固定的样本大小设计.
- 评估性能有1个或3个临时外观.
主要成果:
- 建议在具有有效变体的动力试验中停止OBF.
- 建议固定样本大小设计用于不足的研究.
- 随着徒劳性监测的Pocock边界将预期的样本大小降至最低.
结论:
- 来自模拟的流程图为平台试验设计提供了指导.
- 结果有助于高科技公司进行统计严格的研究.
- 关于停止规则的知情决定可以提高试验效率和资源分配.
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