Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies

Authors

Kristina Vatcheva, The University of Texas Rio Grande ValleyFollow
MinJae Lee
Joseph B. Mccormick, University of Texas Health at HoustonFollow
Mohammad H. Rahbar

Document Type

Article

Publication Date

4-2016

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.

Comments

© 2016 Vatcheva KP, et al.

Recommended 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

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Publication Title

Epidemiology

DOI

10.4172/2161-1165.1000227