Biostatistics Unit, HRB Clinical Research Facility Galway, University of Galway, Galway City, Ireland.
Eur J Epidemiol. 2024 Jul;39(7):715-742. doi: 10.1007/s10654-024-01129-1. Epub 2024 Jul 6.
Here we introduce graphPAF, a comprehensive R package designed for estimation, inference and display of population attributable fractions (PAF) and impact fractions. In addition to allowing inference for standard population attributable fractions and impact fractions, graphPAF facilitates display of attributable fractions over multiple risk factors using fan-plots and nomograms, calculations of attributable fractions for continuous exposures, inference for attributable fractions appropriate for specific risk factor mediator outcome pathways (pathway-specific attributable fractions) and Bayesian network-based calculations and inference for joint, sequential and average population attributable fractions in multi-risk factor scenarios. This article can be used as both a guide to the theory of attributable fraction estimation and a tutorial regarding how to use graphPAF in practical examples.
在这里,我们介绍了 graphPAF,这是一个全面的 R 包,用于估计、推断和展示人群归因分数(PAF)和影响分数。除了允许对标准人群归因分数和影响分数进行推断外,graphPAF 还通过扇形图和诺模图方便地展示了多个风险因素的归因分数,计算了连续暴露的归因分数,对特定风险因素-中介-结果途径(途径特异性归因分数)的归因分数进行推断,并基于贝叶斯网络对多风险因素情况下的联合、顺序和平均人群归因分数进行计算和推断。本文既可以作为归因分数估计理论的指南,也可以作为如何在实际示例中使用 graphPAF 的教程。