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 äny 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.
%0 Journal Article
%1 Greenland2001
%A Greenland, S
%D 2001
%J Epidemiology (Cambridge, Mass.)
%K Bias(Epidemiology) Biometry Child ElectromagneticFields ElectromagneticFields:adverseeffects EnvironmentalExposure Humans PrecursorCellLymphoblasticLeukemia-Lymphoma PrecursorCellLymphoblasticLeukemia-Lymphoma:ep PrecursorCellLymphoblasticLeukemia-Lymphoma:et RiskAssessment Sweden Sweden:epidemiology UnitedStates UnitedStates:epidemiology
%N 5
%P 518-20
%T Attributable fractions: bias from broad definition of exposure.
%U http://www.ncbi.nlm.nih.gov/pubmed/11505170
%V 12
%X 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 äny 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.
%@ 1044-3983; 1044-3983
@article{Greenland2001,
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.},
added-at = {2023-02-03T11:44:35.000+0100},
author = {Greenland, S},
biburl = {https://www.bibsonomy.org/bibtex/276858b45f9a65e17f9cbc70e71276cd4/jepcastel},
city = {Department of Epidemiology, UCLA School of Public Health, Los Angeles, CA, USA.},
interhash = {4d10cbe80e3aefca5fc454ac86e752b2},
intrahash = {76858b45f9a65e17f9cbc70e71276cd4},
isbn = {1044-3983; 1044-3983},
issn = {1044-3983},
journal = {Epidemiology (Cambridge, Mass.)},
keywords = {Bias(Epidemiology) Biometry Child ElectromagneticFields ElectromagneticFields:adverseeffects EnvironmentalExposure Humans PrecursorCellLymphoblasticLeukemia-Lymphoma PrecursorCellLymphoblasticLeukemia-Lymphoma:ep PrecursorCellLymphoblasticLeukemia-Lymphoma:et RiskAssessment Sweden Sweden:epidemiology UnitedStates UnitedStates:epidemiology},
month = {9},
note = {6068<m:linebreak></m:linebreak>LR: 20071115; JID: 9009644; RF: 15; ppublish;<m:linebreak></m:linebreak>Risc atribuïble},
number = 5,
pages = {518-20},
pmid = {11505170},
timestamp = {2023-02-03T11:44:35.000+0100},
title = {Attributable fractions: bias from broad definition of exposure.},
url = {http://www.ncbi.nlm.nih.gov/pubmed/11505170},
volume = 12,
year = 2001
}