Medicine and Health Sciences Commons^™

Multicollinearity In Regression Analyses Conducted In Epidemiologic Studies, Kristina Vatcheva, Minjae Lee, Joseph B. Mccormick, Mohammad H. Rahbar

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

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 …

The Effect Of Ignoring Statistical Interactions In Regression Analyses Conducted In Epidemiologic Studies: An Example With Survival Analysis Using Cox Proportional Hazards Regression Model, Kristina Vatcheva, Joseph B. Mccormick, Mohammad H. Rahbar

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

Objective: To demonstrate the adverse impact of ignoring statistical interactions in regression models used in epidemiologic studies.

Study design and setting: Based on different scenarios that involved known values for coefficient of the interaction term in Cox regression models we generated 1000 samples of size 600 each. The simulated samples and a real life data set from the Cameron County Hispanic Cohort were used to evaluate the effect of ignoring statistical interactions in these models.

Results: Compared to correctly specified Cox regression models with interaction terms, misspecified models without interaction terms resulted in up to 8.95 fold bias in estimated …