Multicollinearity may lead to artificial interaction: an example from a cross sectional study of biomarkers.

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dc.contributor.authorSithisarankul, Pen_US
dc.contributor.authorWeaver, V Men_US
dc.contributor.authorDiener-West, Men_US
dc.contributor.authorStrickland, P Ten_US
dc.date.accessioned2009-05-27T15:05:02Z
dc.date.available2009-05-27T15:05:02Z
dc.date.issued1997-06-01en_US
dc.descriptionThe Southeast Asian Journal of Tropical Medicine and Public Health.en_US
dc.description.abstractCollinearity is the situation which arises in multiple regression when some or all of the explanatory variables are so highly correlated with one another that it becomes very difficult, if not impossible, to disentangle their influences and obtain a reasonably precise estimate of their effects. Suppressor variable is one of the extreme situations of collinearity that one variable can substantially increase the multiple correlation when combined with a variable that is only modestly correlated with the response variable. In this study, we describe the process by which we disentangled and discovered multicollinearity and its consequences, namely artificial interaction, using the data from cross-sectional quantification of several biomarkers. We showed how the collinearity between one biomarker (blood lead level) and another (urinary trans, trans-muconic acid) and their interaction (blood lead level* urinary trans, trans-muconic acid) can lead to the observed artificial interaction on the third biomarker (urinary 5-aminolevulinic acid).en_US
dc.description.affiliationDepartment of Environmental Health Sciences, Johns Hopkins School of Hygiene and Public Health, Baltimore, MD 21205 USA.en_US
dc.identifier.citationSithisarankul P, Weaver VM, Diener-West M, Strickland PT. Multicollinearity may lead to artificial interaction: an example from a cross sectional study of biomarkers. The Southeast Asian Journal of Tropical Medicine and Public Health. 1997 Jun; 28(2): 404-9en_US
dc.identifier.urihttps://imsear.searo.who.int/handle/123456789/31870
dc.language.isoengen_US
dc.source.urihttps://www.tm.mahidol.ac.th/seameo/publication.htmen_US
dc.subject.meshBiological Markers --analysisen_US
dc.subject.meshChilden_US
dc.subject.meshCotinine --urineen_US
dc.subject.meshCreatinine --urineen_US
dc.subject.meshCross-Sectional Studiesen_US
dc.subject.meshEffect Modifiers (Epidemiology)en_US
dc.subject.meshHumansen_US
dc.subject.meshLead --blooden_US
dc.subject.meshLevulinic Acids --urineen_US
dc.subject.meshLinear Modelsen_US
dc.subject.meshStatistics, Nonparametricen_US
dc.titleMulticollinearity may lead to artificial interaction: an example from a cross sectional study of biomarkers.en_US
dc.typeJournal Articleen_US
dc.typeResearch Support, U.S. Gov't, P.H.S.en_US
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