Estimating average attributable fractions with confidence intervals for cohort and case–control studies Academic Article uri icon

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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.

authors

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

publication date

  • April 2018