部分嵌套随机试验的高效设计:最大限度的方法
Math Jjm Candel1, Gerard Jp van Breukelen1,2
1Department of Methodology and Statistics, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands.
Statistical methods in medical research
|March 13, 2026
概括
本研究介绍了随机试验的最大设计,用集群治疗来最大限度地减少受试者和研究成本. 这些设计确保了跨参数范围的所需统计功率,优化了对有效临床研究的资源配置.
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
- 卫生经济学 卫生经济学
背景情况:
- 随机试验与集群臂和连续结果呈现独特的设计挑战.
- 优化样本规模和资源配置对于有效的临床研究至关重要.
- 现有的最佳设计需要精确的参数知识,这往往是不可用的.
研究的目的:
- 开发和呈现高效的研究设计,用于两种治疗随机试验,在一个手臂中进行聚类.
- 尽量减少科目数量和研究预算,同时实现所需的统计能力水平.
- 在设计阶段通过引入最大设计来应对未知的参数的挑战.
主要方法:
- 该研究提出了优化治疗与控制分配比率以及集群数量和集群大小之间的平衡的设计.
- 引入最大限度设计以确保可信的参数范围的预指定功率水平,最大限度地提高最坏情况下的功率.
- 设计还可以用于固定数量的集群或固定集群大小,以适应实际约束.
主要成果:
- 马克西明设计在一系列未知参数中提供了强大的功率保证.
- 这些设计优化了对象的分配和集群的结构 (数量与大小).
- 经验示例表明,与平等分配设计相比,研究预算大幅减少.
结论:
- 马克西明设计提供了一种实用和高效的方法,用于在集群随机试验中优化样本大小和预算.
- 提出的方法提供了一种可靠的方式,尽管参数不确定性,实现所需的统计能力.
- 有R Shiny应用程序可用于方便计算这些实用设计的样本大小.
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