title: Attributable fractions: bias from broad definition of exposure.
abstract: In certain special situations, simplification of an exposure measure into a dichotomy results in no bias from nondifferential misclassification when estimating the attributable fraction for "any exposure." This fact has led to recommendations to use a broad definition of exposure when estimating attributable fractions. I here review the assumptions underlying exposure simplification, focusing on the assumptions that the source and target populations have the same exposure distribution and that complete risk removal is possible. I argue that attributable fraction estimates based on dichotomization can be especially sensitive to violations of these assumptions, and hence misleading for projecting the impact of exposure reduction. I conclude that it is important to obtain and use detailed exposure and covariate information for attributable-fraction estimation.
authors.affiliation: Department of Epidemiology, UCLA School of Public Health, Los Angeles, CA, USA.
authors.forename: S
authors.initials: S
authors.lastname: Greenland
mesh_terms: D015982:Bias; D001699:Biometry; D002648:Child; D004574:Electromagnetic Fields / Q000009:adverse effects*; D004781:Environmental Exposure; D006801:Humans; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000453:epidemiology / Q000209:etiology*; D018570:Risk Assessment; D013548:Sweden / Q000453:epidemiology; D014481:United States / Q000453:epidemiology