Estimating and displaying population attributable fractions using the R package: graphPAF

John Ferguson, Maurice O'Connell

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
JournalEuropean journal of epidemiology
DOIs
Publication statusPublished - 6 Jul 2024

Keywords

  • Population attributable fraction
  • Impact fraction
  • Continuous exposure
  • Directed acyclic graph
  • Bayesian network

Fingerprint

Dive into the research topics of 'Estimating and displaying population attributable fractions using the R package: graphPAF'. Together they form a unique fingerprint.

Cite this