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A Comparison of Approaches to Control for Confounding Factors by Regression ModelsXing G.a · Lin C.-Y.b · Xing C.b, c
aBristol-Myers Squibb Company, Pennington, N.J., bMcDermott Center of Human Growth and Development and cDepartment of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Tex., USA Corresponding Author
Chao Xing, PhD
MC 8591, University of Texas Southwestern Medical Center
5323 Harry Hines Boulevard
Dallas, TX 75390 (USA)
Tel. +1 214 648 1695, E-Mail firstname.lastname@example.org
A common technique to control for confounding factors in practice is by regression adjustment. There are various versions of regression modeling in the literature, and in this paper we considered four approaches often seen in genetic association studies. We carried out both analytical and simulation studies comparing the bias of effect size estimates and examining the test sizes under the null hypothesis of no association between an outcome and an exposure. Further, we compared the methods in a nonsynonymous genome-wide scan for plasma lipoprotein(a) levels using a dataset from the Dallas Heart Study. We found that a widely employed approach that models the covariate-adjusted outcome and the exposure leads to an infranominal test size and underestimation of the exposure effect size. In conclusion, we recommend either using multiple regression models or modeling the covariate-adjusted outcome and the covariate-adjusted exposure to control for confounding factors.
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