集群随机试验和个人随机组治疗试验的高效设计
Math J J M Candel1, Gerard J P van Breukelen1
1Department of Methodology and Statistics, Care and Public Health Research Institute (CAPHRI), Maastricht University.
Psychological methods
|February 13, 2025
概括
本研究介绍了集群随机试验的高效设计,以尽量减少受试者或预算,同时保持所需的统计能力. 提出了Maximin设计,以确保即使在未知参数的情况下也能提供电力,帮助研究人员进行样本大小计算.
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
- 医疗保健服务研究 医疗服务研究
背景情况:
- 集群随机试验 (CRT) 和个人随机组治疗试验 (IRGT) 是常见的用于比较两个治疗在连续的结果.
- 确定最佳样本大小和资源配置对于在这些复杂的试验设计中实现所需的统计能力至关重要.
研究的目的:
- 提出设计,尽量减少CRT和IRGT的科目数量或研究预算,同时达到指定的功率水平.
- 引入最大限度的设计,以确保在可信的范围内的未知参数,特别是结果差异的预规定的功率水平.
主要方法:
- 该研究审查并扩展了关于CRT和IRGT最佳和最大设计的现有文献.
- 针对由于实际约束而固定的集群/组数量的场景,提出了最佳和最大设计的导出.
- 开发了一个交互式R Shiny应用程序,以方便各种maximin设计的样本大小计算.
主要成果:
- 提出了最佳设计,以平衡治疗与控制的分配比率和参与者在集群/组之间以及集群/组内部的分布.
- 马克西明设计提供了一个强大的方法来确定样本大小,当关键参数,如结果差异,在设计阶段是未知的.
- 该研究使用实证示例说明了样本大小计算和潜在的预算节省.
结论:
- 开发的设计和工具为优化集群随机化和组随机化试验中的资源分配提供了实际解决方案.
- 极限设计通过考虑参数不确定性来提高功率计算的可靠性.
- 该R Shiny应用程序为研究人员提供了可访问的功能,以实施这些先进的设计策略.
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