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

John Ferguson, Maurice O’Connell

Research output: Contribution to a Journal (Peer & Non Peer)Articlepeer-review

9 Citations (Scopus)

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
Pages (from-to)715-742
Number of pages28
JournalEuropean Journal of Epidemiology
Volume39
Issue number7
DOIs
Publication statusPublished - Jul 2024

Keywords

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

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