Title |
Estimating average attributable fractions with confidence intervals for cohort and case–control studies
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Published in |
Statistical Methods in Medical Research, June 2016
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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. |
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