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Estimating average attributable fractions with confidence intervals for cohort and case–control studies

Overview of attention for article published in Statistical Methods in Medical Research, June 2016
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Title
Estimating average attributable fractions with confidence intervals for cohort and case–control studies
Published in
Statistical Methods in Medical Research, June 2016
DOI 10.1177/0962280216655374
Pubmed ID
Authors

John Ferguson, Alberto Alvarez-Iglesias, John Newell, John Hinde, Martin O’Donnell

Abstract

Chronic diseases tend to depend on a large number of risk factors, both environmental and genetic. Average attributable fractions were introduced by Eide and Gefeller as a way of partitioning overall disease burden into contributions from individual risk factors; this may be useful in deciding which risk factors to target in disease interventions. Here, we introduce new estimation methods for average attributable fractions that are appropriate for both case-control designs and prospective studies. Confidence intervals, derived using Monte Carlo simulation, are also described. Finally, we introduce a novel approximation for the sample average attributable fraction that will ensure a computationally tractable approach when the number of risk factors is large. An R package, [Formula: see text], implementing the methods described in this manuscript can be downloaded from the CRAN repository.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 23%
Student > Ph. D. Student 3 23%
Professor 2 15%
Student > Master 2 15%
Student > Bachelor 2 15%
Other 0 0%
Unknown 1 8%
Readers by discipline Count As %
Medicine and Dentistry 6 46%
Nursing and Health Professions 2 15%
Mathematics 2 15%
Agricultural and Biological Sciences 1 8%
Biochemistry, Genetics and Molecular Biology 1 8%
Other 0 0%
Unknown 1 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 June 2016.
All research outputs
#9,460,814
of 11,842,921 outputs
Outputs from Statistical Methods in Medical Research
#357
of 663 outputs
Outputs of similar age
#189,519
of 270,723 outputs
Outputs of similar age from Statistical Methods in Medical Research
#21
of 41 outputs
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