Work

Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies

Year: 2016

Type: article

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.

Source: Epidemiology Open Access

DOI: 10.4172/2161-1165.1000227

Authors: Kristina P. Vatcheva, MinJae Lee

Institution: The University of Texas Health Science Center at Houston

Language: English

Topic: Statistical Methods in Epidemiology

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funders: National Institutes of Health, Center for Clinical and Translational Sciences, University of Texas Health Science Center at Houston, National Center for Advancing Translational Sciences