title: Model-based estimation of the attributable fraction for cross-sectional, case-control and cohort studies using the R package AF.
abstract: The attributable fraction (or attributable risk) is a widely used measure that quantifies the public health impact of an exposure on an outcome. Even though the theory for AF estimation is well developed, there has been a lack of up-to-date software implementations. The aim of this article is to present a new R package for AF estimation with binary exposures. The package AF allows for confounder-adjusted estimation of the AF for the three major study designs: cross-sectional, (possibly matched) case-control and cohort. The article is divided into theoretical sections and applied sections. In the theoretical sections we describe how the confounder-adjusted AF is estimated for each specific study design. These sections serve as a brief but self-consistent tutorial in AF estimation. In the applied sections we use real data examples to illustrate how the AF package is used. All datasets in these examples are publicly available and included in the AF package, so readers can easily replicate all analyses.
authors.affiliation: Karolinska Institute, Nobels väg 12A, 171 77, Stockholm, Sweden. elisabeth.dahlqwist@ki.se.
authors.forename: Elisabeth
authors.identifier: 0000-0001-5797-6803
authors.initials: E
authors.lastname: Dahlqwist
authors.affiliation: Karolinska Institute, Nobels väg 12A, 171 77, Stockholm, Sweden.
authors.forename: Johan
authors.initials: J
authors.lastname: Zetterqvist
authors.affiliation: Karolinska Institute, Nobels väg 12A, 171 77, Stockholm, Sweden.
authors.forename: Yudi
authors.initials: Y
authors.lastname: Pawitan
authors.affiliation: Karolinska Institute, Nobels väg 12A, 171 77, Stockholm, Sweden.
authors.forename: Arvid
authors.initials: A
authors.lastname: Sjölander
mesh_terms: D016022:Case-Control Studies; D015331:Cohort Studies; D015986:Confounding Factors, Epidemiologic; D003430:Cross-Sectional Studies; D004812:Epidemiologic Methods; D006801:Humans; D018570:Risk Assessment / Q000379:methods
keywords: Attributable fraction; Attributable risk; Confounder-adjusted; Public health; R package; Regression model; Statistical software