对关联结构对多个二进制结果的统计方法样本大小要求的影响:模拟研究
Kanako Fuyama1, Kentaro Sakamaki2, Kohei Uemura3
1Department of Biostatistics, Graduate School of Medicine, Hokkaido University, Sapporo, Japan.
Clinical trials (London, England)
|January 3, 2025
概括
了解结果相关性对于在临床试验中选择统计方法至关重要. 优先结果方法往往提供更高的功率和更小的样本大小,特别是当相关性在治疗臂之间一致时.
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
- 统计方法 统计方法
背景情况:
- 随机临床试验越来越多地使用复杂的方法来处理多个二进制结果.
- 结果相关性影响样本大小要求,但它们对统计能力和样本大小的影响需要进一步研究.
研究的目的:
- 为了比较不同统计方法在不同相关性结构下分析多个二进制结果的功率和样本大小.
- 评估共同主终点,复合终点和优先结果方法.
主要方法:
- 使用模拟来评估统计能力和样本大小要求.
- 通过不同的相关性,边际比例,治疗效果和结果数量来评估方法.
- 一个涉及偏头痛治疗试验的案例研究进行了样本大小分析.
主要成果:
- 相关性显著影响了复合终点的功率和样本大小.
- 同主终点在相关性中显示稳定的功率,但在相反的治疗效果或>2组件的情况下下降.
- 优先结果方法通常产生更高的功率和更小的样本大小,当关联在双臂之间是相似的.
结论:
- 在选择多个二进制结果的统计方法时,预测和考虑相关性至关重要.
- 同主终点可靠地证明优越性,但不能平衡相反的治疗效果.
- 一般化的对比比较为优先结果提供了有效的替代方案,在共享相关性时,通常会最小化样本大小.
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