alexa The Effect of Ignoring Statistical Interactions in Regression Analyses Conducted in Epidemiologic Studies: An Example with Survival Analysis Using Cox Proportional Hazards Regression Model
ISSN: 2161-1165

Epidemiology: Open Access
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Research Article

The Effect of Ignoring Statistical Interactions in Regression Analyses Conducted in Epidemiologic Studies: An Example with Survival Analysis Using Cox Proportional Hazards Regression Model

Vatcheva KP1, Lee M3,4, McCormick JB1and Rahbar MH2,3,4*

1Division of Epidemiology, University of Texas Health Science Center-Houston, School of Public Health, Brownsville Campus, Brownsville, TX, USA

2Department of Epidemiology, Human Genetics, and Environmental Sciences (EHGES), University of Texas School of Public Health at Houston, Houston, TX, USA

3Division of Clinical and Translational Sciences, Department of Internal Medicine, Medical School; The University of Texas Health Science Center at Houston, Houston, TX, USA

4Biostatistics/Epidemiology/Research Design (BERD) Core, Center for Clinical and Translational Sciences (CCTS), The University of Texas Health Science Center at Houston, Houston, TX, USA

Corresponding Author:
Mohammad H. Rahbar PhD
University of Texas Health Science Center at Houston
Biostatistics/Epidemiology/Research Design
Component of Center for Clinical and Translational Sciences
6410 Fannin Street, UT Professional Building Suite 1100.05
Houston, TX 77030, USA
Tel: (713)500-7901
Fax: (713)500-0766
E-mail: [email protected]

Received Date: November 01, 2014 Accepted Date: January 12, 2015 Published Date: January 15, 2015

Citation:Vatcheva KP, Lee M, McCormick JB, Rahbar MH (2016) The Effect of Ignoring Statistical Interactions in Regression Analyses Conducted in Epidemiologic Studies: An Example with Survival Analysis Using Cox Proportional Hazards Regression Model. Epidemiol 6: 216. doi:10.4172/2161-1165.1000216

Copyright: © 2016 Vatcheva KP, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

 

Abstract

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 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.

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