在给定的预算下对集群随机试验中的最佳设计的概述,具有二进制结果
Jingxia Liu1,2, Lei Liu2, Aimee S James1
1Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine (WUSM), St Louis, Missouri, USA.
Statistical methods in medical research
|June 7, 2023
概括
开发最佳的集群随机试验设计可以通过尽量减少预算约束中的差异来解决财务问题. 这项研究引入了新的方法和SAS宏,用于有效的试验规划.
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:
- 集群随机试验 (CRT) 每个集群的成本比每个受试者的成本更高.
- 优化设计对于平衡CRT的成本和统计效率至关重要.
- 一般估计方程 (GEE) 模型用于CRT分析.
研究的目的:
- 开发最佳的集群随机试验设计,尽量减少预算限制下的差异.
- 为平行和部分嵌套的CRT提出本地最佳和MaxiMin设计.
- 为实现这些最佳设计提供实用工具 (SAS宏).
主要方法:
- 使用具有工作相关性结构的通用估计方程 (GEE) 模型R(ρ).
- 根据相关性参数范围和入学可行性定义参数和设计空间.
- 计算相对效率,并确定最大限度地提高最小相对效率的MaxiMin设计.
主要成果:
- 总结并提出了当地最佳和MaxiMin设计,用于具有预先确定和未决定的组分配比例的双层和三层并行CRT.
- 开发了部分嵌套CRT的最佳设计,具有可交换的关联结构.
- 创建了三个新的和更新了两个现有的SAS宏,以实现最佳的CRT设计.
结论:
- 该研究为最佳CRT设计提供了可靠的方法,提高了统计能力和成本效益.
- 新的SAS宏便于先进的最佳设计技术的实际应用.
- 这些进展支持更有效,更可靠的临床试验研究.
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