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Tuesday, June 28 • 5:03pm - 5:21pm
'AF' a new package for estimating the attributable fraction

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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.

avatar for Jacqueline Meulman

Jacqueline Meulman

Visiting Professor, Stanford University

avatar for Elisabeth Dahlqwist

Elisabeth Dahlqwist

Karolinska Institute
I am a Phd student at Karolinska Institutet, Department of medical epidemiology and biostatistics. The topic of my PhD is methodological developments of the attributable fraction. My first project have been to implement a R package which unifies methods for estimating the model-based attributable fraction for the study desigs: cross-sectional, case-control and cohort (with time-to-event outcomes). In my talk I will give an introduction to the... Read More →

Tuesday June 28, 2016 5:03pm - 5:21pm
McCaw Hall 326 Galvez Street Stanford, CA 94305-6105

Attendees (37)