Abstract
ObjectiveTo demonstrate the adverse impact of ignoring statistical interactions in regression models used in epidemiologic studies.Study design and settingBased 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.ResultsCompared to correctly specified Cox regression models with interaction terms, misspecified models without interaction terms resulted in up to 8.95 fold bias in estimated regression coefficients. Whereas when data were generated from a perfect additive Cox proportional hazards regression model the inclusion of the interaction between the two covariates resulted in only 2% estimated bias in main effect regression coefficients estimates, but did not alter the main findings of no significant interactions.ConclusionsWhen the effects are synergic, the failure to account for an interaction effect could lead to bias and misinterpretation of the results, and in some instances to incorrect policy decisions. Best practices in regression analysis must include identification of interactions, including for analysis of data from epidemiologic studies.
Highlights
The terms interaction effect and effect modification, or effect-measure modification are often used interchangeably, for health related research in epidemiology [1]
The authors detected a significant interaction between leptin levels and obesity, concluded that obesity modifies the effect of leptin on kidney function decline in patients with type 2 diabetes
To demonstrate the importance of including interactions in regression models in real life epidemiologic studies, we compared results from additive and interactive models using data from the Cameron County Hispanic Cohort (CCHC) [8]. For this we focused on investigating the role of peripheral white blood cells (WBC) counts, WBC differential counts and Body Mass Index (BMI) in association with the time to incidence of type 2 diabetes while controlling for the effect of other known type 2 diabetes risk factors
Summary
The terms interaction effect and effect modification, or effect-measure modification are often used interchangeably, for health related research in epidemiology [1]. Using data from an Epidemiologic Study on the Insulin Resistance Syndrome (DESIR) cohort, Gautier A. et al (2010) found significant interactions between baseline BMI categories and higher waist circumference in relation to progression to type 2 diabetes using a logistic regression model [3] Based on their findings the authors concluded that for reducing incidence of type 2 diabetes in their study population, it is important to monitor and prevent increases in waist circumference, for those with BMI
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