Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies
Received Date: Feb 15, 2016 / Accepted Date: Feb 29, 2016 / Published Date: Mar 07, 2016
Abstract
The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers to consider the diagnostic for multicollinearity as one of the steps in regression analysis.
Keywords: Multicollinearity; Regression analysis; Simulation; BMI; Waist circumference
Citation: Vatcheva KP, Lee M, McCormick JB, Rahbar MH (2016) Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies. Epidemiol 6:227. Doi: 10.4172/2161-1165.1000227
Copyright: © 2016 Vatcheva KP, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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