Estimating average attributable fractions with confidence intervals for cohort and case–control studies. (April 2018)

• Record Type: Journal Article
• Authors: Ferguson, John; Alvarez-Iglesias, Alberto; Newell, John; Hinde, John; O'Donnell, Martin
• Is Part Of: Statistical methods in medical research. Volume 27:Number 4(2018)
• Page Start: 1141
• Page End: 1152
• Publication Date: 2018-04
• Subjects: Epidemiology -- attributable fraction -- permutations -- weighted likelihood -- Monte Carlo confidence interval; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280216655374
• Languages: English
• ISSNs: 0962-2802

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.