使用R包估计和显示人口归因分数:图PAFFPAF
John Ferguson1, Maurice O'Connell2
1Biostatistics Unit, HRB Clinical Research Facility Galway, University of Galway, Galway City, Ireland. john.ferguson@universityofgalway.ie.
European journal of epidemiology
|July 6, 2024
概括
本研究介绍了graphPAF,这是一个用于估计和显示人口归因分数 (PAF) 和冲击分数的R包. 它提供了用于多重风险因素分析,路径特定计算和持续暴露的高级功能,有助于公共卫生研究.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 人口归因分数 (PAF) 对于了解风险因素对公共卫生的影响至关重要.
- 现有的PAF估计和可视化方法可能是有限的,特别是在复杂的多重风险因素场景中.
研究的目的:
- 引入graphPAF,一个新的R包,用于PAF和冲击分数的全面估计,推断和显示.
- 为高级PAF分析提供工具,包括连续暴露,途径特定的分数和多重风险因素场景.
主要方法:
- 图表PAF R包的开发.
- 实施标准和高级PAF计算方法.
- 整合可视化工具,如风扇图和名ograms.
- 对复杂的多重风险因素场景应用贝叶斯网络方法.
主要成果:
- graphPAF可以对各种类型的归因分数进行可靠的估计和推断.
- 该套件有助于清晰可视化可归因分数,包括多个风险因素.
- 它支持连续暴露和特定路径的归因分数的计算.
- 贝叶斯网络方法用于复杂环境中的联合,序列和平均PAF.
结论:
- 图形PAF是一个全面的R包,它增强了估计,推断和显示人口归因分数的功能.
- 该包为流行病学家和生物统计学家提供了有价值的工具,以评估风险因素对公共卫生的影响.
- 图形PAF既是理论指南,也是归因分数分析的实践教程.
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