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.
Highlights
Multicollinearity arises when at least two highly correlated predictors are assessed simultaneously in a regression model
Using data from the Cameron County Hispanic Cohort (CCHC), we demonstrated the effect of multicollinearity caused by Body Mass Index (BMI) and waist circumference (WC) on two outcome variables a) systolic blood pressure and b) diastolic blood pressure in two separate linear regression analyses
When body mass index (BMI) and waist circumference were assessed in separate models, while other covariates kept in the model, their variance inflation factor (VIF) were 1.01 and 1.06, respectively
Summary
Multicollinearity arises when at least two highly correlated predictors are assessed simultaneously in a regression model. When the predictor variables are highly correlated the common interpretation of a regression coefficient of one predictor as measuring the change in expected value of the response variable due to one unit increase in that predictor variable when holding the other predictors constant may be practically impossible [14] These can lead to misleading conclusions for the role of each of the collinear predictors in the regression model. The authors reported that they had limited ability to conclude whether the observed association is specific for α-carotene due to a high degree of collinearity between the plasma carotenoids As another example, in order to develop efficient public health interventions addressing the obesity epidemic, Leal et al [16] had a methodological challenge to disentangle the effects of highly correlated neighborhood characteristics and identify exactly which aspects of the environments (physical and service) influence obesity risk. Individual/neighborhood socioeconomic adjusted physical and service-related neighborhood characteristics were inversely associated with BMI/waist circumference, but the authors reported that they were unable to determine which one of these factors had an independent effect on BMI/waist circumference [16]
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