title: Reverse attenuation in interaction terms due to covariate measurement error.
abstract: Covariate measurement error may cause biases in parameters of regression coefficients in generalized linear models. The influence of measurement error on interaction parameters has, however, only rarely been investigated in depth, and if so, attenuation effects were reported. In this paper, we show that also reverse attenuation of interaction effects may emerge, namely when heteroscedastic measurement error or sampling variances of a mismeasured covariate are present, which are not unrealistic scenarios in practice. Theoretical findings are illustrated with simulations. A Bayesian approach employing integrated nested Laplace approximations is suggested to model the heteroscedastic measurement error and covariate variances, and an application shows that the method is able to reveal approximately correct parameter estimates.
authors: Epidemiology, Biostatistics, and Prevention Institute, University of Zurich, Hirschengraben 84, 8001 Zurich, Switzerland.|Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland.
authors: Stefanie
authors: S
authors: Muff
authors: Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland.
authors: Lukas F
authors: LF
authors: Keller
mesh_terms: D000704:Analysis of Variance; D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D001794:Blood Pressure; D006331:Heart Diseases / Q000453:epidemiology / Q000503:physiopathology; D006801:Humans; D012107:Research Design
keywords: Attenuation; Bayesian analysis; Berkson error; Classical error; Heteroscedastic error