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引用次数: 0
摘要
这里我们介绍 graphPAF，它是一个综合性 R 软件包，用于估计、推断和显示人口可归因分数（PAF）和影响分数。除了可以推断标准的人群可归因分数和影响分数外，graphPAF 还便于使用扇形图和提名图显示多种风险因素的可归因分数，计算连续暴露的可归因分数，推断适合特定风险因素→介导因素→结果途径的可归因分数（途径特异性可归因分数），以及基于贝叶斯网络计算和推断多风险因素情况下的联合、连续和平均人群可归因分数。本文既可作为归因分数估计理论的指南，也可作为如何在实际例子中使用 graphPAF 的教程。
Estimating and displaying population attributable fractions using the R package: graphPAF.
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.
期刊介绍：
The European Journal of Epidemiology, established in 1985, is a peer-reviewed publication that provides a platform for discussions on epidemiology in its broadest sense. It covers various aspects of epidemiologic research and statistical methods. The journal facilitates communication between researchers, educators, and practitioners in epidemiology, including those in clinical and community medicine. Contributions from diverse fields such as public health, preventive medicine, clinical medicine, health economics, and computational biology and data science, in relation to health and disease, are encouraged. While accepting submissions from all over the world, the journal particularly emphasizes European topics relevant to epidemiology. The published articles consist of empirical research findings, developments in methodology, and opinion pieces.
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