设计以自我为中心的基于网络的研究,以估计干扰下的因果关系
Junhan Fang1, Donna Spiegelman2,3, Ashley L Buchanan4
1Hoffmann-La Roche Ltd, Mississauga, ON, Canada.
Statistical methods in medical research
|July 17, 2025
概括
本研究引入了一种方法,用于测量基于网络的公共卫生干预措施中的个人和溢出效应. 它提供了样本大小公式,用于设计有效的同行教育研究,例如预防艾滋病毒的研究.
科学领域:
- 公共卫生研究 公共卫生研究
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 公共卫生干预通常发生在连接的群体中,导致潜在的溢出效应.
- 评估个体,溢出和整体影响对于理解社交网络中的干预影响至关重要.
- 以自我为中心的网络设计对于利用同行影响的干预措施来说是常见的,例如预防艾滋病毒的干预措施.
研究的目的:
- 澄清在以自我中心网络为基础的随机设计中识别因果关系的假设.
- 开发样本大小公式来估计个人,溢出和整体效应.
- 为设计和分析基于网络的公共卫生干预提供框架.
主要方法:
- 利用潜在结果框架来定义和识别因果关系.
- 采用了一个回归模型,具有块对角结构,用于共同估计效应.
- 关于干预效应的单个和联合假设测试的衍生样本大小公式.
主要成果:
- 根据对自我中心网络设计的澄清假设建立了识别策略.
- 开发了适用于各种基于网络的干预研究的实用样本大小公式.
- 使用HIV预防同行教育干预的例子证明了公式的实用性.
结论:
- 拟议的方法允许对网络环境中的个人和溢出效应进行可靠的估计.
- 样本大小公式是优化基于网络的公共卫生研究设计和功率的重要工具.
- 这项工作支持严格评估依赖于社会影响的干预措施,例如对艾滋病毒预防的同行教育.
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