Using Adjusted Relative Risks to Calculate Attributable Fractions
Year: 2006
Type: article
Abstract: Leveille et al.1 estimated population attributable fraction (PAF) values for arthritis attributable to obesity by using multivariate-adjusted relative risks and the body mass index distribution in the US population aged 40 through 74 years. It is important to adjust relative risk estimates for confounding factors such as age and gender that are associated with both arthritis and obesity. However, when relative risks are adjusted for confounding factors, it is also necessary to use properly adjusted estimators of attributable fraction to avoid bias.
The PAF formula cited by Leveille et al. is appropriate only for use with relative risks that are unadjusted for confounding2; the use of adjusted relative risks in this formula is incorrect and could lead to bias.3–5 There are ways to produce attributable fraction estimates that are properly adjusted for confounding and effect modification.3,4 In addition, Leveille et al. used the method of Zhang and Yu6 to calculate a relative risk estimate from a multivariate-adjusted odds ratio. This method is not valid when the estimated odds ratio is adjusted for confounding factors.7
Leveille et al. compared risk estimates and PAF values over time from the series of National Health and Nutrition Examination Surveys (NHANES), but without confidence intervals or statistical tests. Methods for statistical inference using data from multiple cross-sectional surveys are available.8 A recent article provides a method of calculating standard errors for attributable fractions from surveys with complex sample designs such as that of the NHANES.9
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1978539488