用图形方法优化临床试验的样本大小,用于多重性调整
Fengqing Zhang1, Jiangtao Gou2
1Department of Psychological and Brain Sciences, Drexel University, Philadelphia, Pennsylvania.
Statistics in medicine
|September 20, 2023
概括
本研究介绍了使用图形方法优化临床试验样本大小的统计方法,用于具有多个终点的临床试验. 它展示了如何平衡临床偏好与功率要求,提高试验设计效率.
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
- 统计学方法论 统计学方法论
背景情况:
- 在具有多个终点的临床试验中,图形方法用于多重性调整.
- 赋予初始权重和过渡概率通常是基于临床重要性,但缺乏对样本大小优化理论指导.
- 临床偏好可能是优化样本大小的限制,但最佳规范仍然是一个挑战.
研究的目的:
- 在临床试验的图形方法中提出优化样本大小超过初始重量和过渡概率的统计方法.
- 为指定权重和概率提供理论指导,以满足临床偏好,同时尽量减少样本大小.
- 为了解决在多终点试验设计中平衡临床重要性与统计效率的既定方法的缺乏.
主要方法:
- 开发了统计方法,通过考虑图形方法的初始重量和过渡概率来优化样本大小.
- 用于每个端点的边际功率作为优化常见设置.
- 证明了在连续性假设下边际权力精确匹配预先规定的值的最佳解决方案的存在.
主要成果:
- 证明一些常见的图形方法,如将所有初始权重集中在单个终点上,是次优的.
- 建议适用于单臂和随机对照试验的灵活方法,具有各种终点类型 (连续,二进制,混合).
- 展示了两种假设临床试验设计的方法的应用,扩展到三个或更多的终点.
结论:
- 提出的方法为设计图形方法和计算多终点临床试验中的样本大小提供了指导.
- 优化初始权重和过渡概率导致更高效的试验设计.
- 这些发现支持通过平衡临床相关性和统计能力来更好地确定样本大小.
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