Estimating average attributable fractions with confidence intervals for cohort and case control studies
Ferguson, John ; Alvarez-Iglesias, Alberto ; Newell, John ; Hinde, John ; O’Donnell, Martin
Ferguson, John
Alvarez-Iglesias, Alberto
Newell, John
Hinde, John
O’Donnell, Martin
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Publication Date
2016-06-24
Type
Article
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Citation
Ferguson, John, Alvarez-Iglesias, Alberto, Newell, John, Hinde, John, & O’Donnell, Martin. (2018). Estimating average attributable fractions with confidence intervals for cohort and case–control studies. Statistical Methods in Medical Research, 27(4), 1141-1152. doi: 10.1177/0962280216655374
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, averisk, implementing the methods described in this manuscript can be downloaded from the CRAN repository.
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Publisher
SAGE Publications
Publisher DOI
10.1177/0962280216655374
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Attribution-NonCommercial-NoDerivs 3.0 Ireland