%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 }