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

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Summary

This study introduces graphPAF, an R package for estimating and displaying population attributable fractions (PAF) and impact fractions. It offers advanced features for multi-risk factor analysis, pathway-specific calculations, and continuous exposures, aiding public health research.

Keywords:
Bayesian networkContinuous exposureDirected acyclic graphImpact fractionPopulation attributable fraction

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Population attributable fractions (PAF) are crucial for understanding the public health impact of risk factors.
  • Existing methods for PAF estimation and visualization can be limited, especially in complex multi-risk factor scenarios.

Purpose of the Study:

  • To introduce graphPAF, a novel R package for comprehensive estimation, inference, and display of PAF and impact fractions.
  • To provide tools for advanced PAF analyses, including continuous exposures, pathway-specific fractions, and multi-risk factor scenarios.

Main Methods:

  • Development of the graphPAF R package.
  • Implementation of methods for standard and advanced PAF calculations.
  • Integration of visualization tools such as fan-plots and nomograms.
  • Application of Bayesian network approaches for complex multi-risk factor scenarios.

Main Results:

  • graphPAF enables robust estimation and inference for various types of attributable fractions.
  • The package facilitates clear visualization of attributable fractions, including over multiple risk factors.
  • It supports calculations for continuous exposures and pathway-specific attributable fractions.
  • Bayesian network methods are incorporated for joint, sequential, and average PAF in complex settings.

Conclusions:

  • graphPAF is a comprehensive R package that enhances the estimation, inference, and display of population attributable fractions.
  • The package provides valuable tools for epidemiologists and biostatisticians to assess the public health impact of risk factors.
  • graphPAF serves as both a theoretical guide and a practical tutorial for attributable fraction analysis.