在集群随机交叉和阶梯试验中使用概括估计方程的最佳设计.
1Division of Public Health Sciences, Department of Surgery and Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA.
Statistical methods in medical research
|May 30, 2024
概括
本研究介绍了集群随机交叉和阶段试验的最佳设计方法,这对于高效的医疗保健研究至关重要. 它提供算法和SAS宏来确定最佳的集群数量和大小,以获得最大的治疗效果估计效率.
科学领域:
- 生物统计学 生物统计学
- 医疗保健服务研究 医疗服务研究
- 实施科学 实施科学
背景情况:
- 纵向集群随机试验,包括集群随机交叉 (CRC) 和阶梯集群随机试验 (SW-CRT),在医疗保健提供和实施科学中至关重要.
- 虽然在CRC和SW-CRT中存在估计处理效应的方法,但对最大效率的最佳设计的指导是有限的.
研究的目的:
- 为多期CRC和SW-CRT开发最佳设计策略,以持续的结果.
- 确定最佳的集群周期大小和集群数量,在预算限制下实现最大效率.
主要方法:
- 开发本地最佳设计算法,假设已知的相关性参数.
- 为未知相关性参数提供MaxiMin最佳设计算法的建议,使用对整数估计的受约束优化.
- 在CRC试验中,用于本地和MaxiMin最佳设计的闭式公式的导出.
- 开发四个SAS宏用于实际实施.
主要成果:
- 为CRC和SW-CRT衍生出最佳设计算法和封闭式公式.
- 该研究涉及封闭队列和重复横截面采样方案.
- 约束优化技术被独特地应用,以获得MaxiMin最佳设计的整数估计.
结论:
- 开发的方法和算法为CRC和SW-CRT的高效设计提供了关键的指导.
- SAS宏在研究中促进了这些最佳设计策略的实际应用.
- 这项工作提高了复杂的纵向集群随机试验中治疗效果估计的效率.
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