title: The Effect of Ignoring Statistical Interactions in Regression Analyses Conducted in Epidemiologic Studies: An Example with Survival Analysis Using Cox Proportional Hazards Regression Model.
abstract: OBJECTIVE
  To demonstrate the adverse impact of ignoring statistical interactions in regression models used in epidemiologic studies.
  
  
  METHODS
  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 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.
  
  
  CONCLUSIONS
  When 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.
authors.affiliation: Division of Epidemiology, University of Texas Health Science Center-Houston, School of Public Health, Brownsville Campus, Brownsville, TX, USA.
authors.forename: K P
authors.initials: KP
authors.lastname: Vatcheva
authors.affiliation: Division of Clinical and Translational Sciences, Department of Internal Medicine, Medical School; The University of Texas Health Science Center at Houston, Houston, TX, USA; Biostatistics/Epidemiology/Research Design (BERD) Core, Center for Clinical and Translational Sciences (CCTS), The University of Texas Health Science Center at Houston, Houston, TX, USA.
authors.forename: M
authors.initials: M
authors.lastname: Lee
authors.affiliation: Division of Epidemiology, University of Texas Health Science Center-Houston, School of Public Health, Brownsville Campus, Brownsville, TX, USA.
authors.forename: J B
authors.initials: JB
authors.lastname: McCormick
authors.affiliation: Department of Epidemiology, Human Genetics, and Environmental Sciences (EHGES), University of Texas School of Public Health at Houston, Houston, TX, USA; Division of Clinical and Translational Sciences, Department of Internal Medicine, Medical School; The University of Texas Health Science Center at Houston, Houston, TX, USA; Biostatistics/Epidemiology/Research Design (BERD) Core, Center for Clinical and Translational Sciences (CCTS), The University of Texas Health Science Center at Houston, Houston, TX, USA.
authors.forename: M H
authors.initials: MH
authors.lastname: Rahbar
keywords: Cox proportional hazards model; Effect modification; Regression analysis; Simulation; Statistical interaction; Type 2 diabetes