%0 Journal Article %1 Dahlqwist2016 %A Dahlqwist, Elisabeth %A Zetterqvist, Johan %A Pawitan, Yudi %A Sjölander, Arvid %D 2016 %J European journal of epidemiology %K Attributablefraction Attributablerisk Confounder-adjusted Publichealth Regressionmodel Rpackage Statisticalsoftware %N 6 %P 575-82 %R 10.1007/s10654-016-0137-7 %T Model-based estimation of the attributable fraction for cross-sectional, case-control and cohort studies using the R package AF. %U http://www.ncbi.nlm.nih.gov/pubmed/26992709 %V 31 %X 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.

@article{Dahlqwist2016, 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.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Dahlqwist, Elisabeth and Zetterqvist, Johan and Pawitan, Yudi and Sjölander, Arvid}, biburl = {https://www.bibsonomy.org/bibtex/230a7756dbf947864ff8759632a1c4cfe/jepcastel}, doi = {10.1007/s10654-016-0137-7}, interhash = {0586a6f8a58b24498fe22e929122e383}, intrahash = {30a7756dbf947864ff8759632a1c4cfe}, issn = {1573-7284}, journal = {European journal of epidemiology}, keywords = {Attributablefraction Attributablerisk Confounder-adjusted Publichealth Regressionmodel Rpackage Statisticalsoftware}, month = {6}, note = {Risc atribuïble; Observacionals; R}, number = 6, pages = {575-82}, pmid = {26992709}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Model-based estimation of the attributable fraction for cross-sectional, case-control and cohort studies using the R package AF.}, url = {http://www.ncbi.nlm.nih.gov/pubmed/26992709}, volume = 31, year = 2016 }