Abstract：
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.
摘要：
在这里我们介绍图PAF，为估算而设计的全面R包，人口归因分数(PAF)和影响分数的推断和显示。除了允许对标准群体归因分数和影响分数进行推断之外，图PAF有助于使用扇形图和列线图显示多个风险因素的可归因分数，连续暴露的可归因分数的计算，推断适用于特定风险因素→介体→结果途径（途径特定归因分数）和基于贝叶斯网络的计算和推断联合，多风险因素情景中的序贯和平均人口归因分数。本文既可以用作归因分数估计理论的指南，也可以用作有关如何在实际示例中使用graphPAF的教程。
