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Deffner, V.* ; Küchenhoff, H.* ; Breitner-Busch, S. ; Schneider, A.E. ; Cyrys, J. ; Peters, A.

Mixtures of Berkson and classical covariate measurement error in the linear mixed model: Bias analysis and application to a study on ultrafine particles.

Biom. J. 60, 480-497 (2018)
Postprint DOI PMC
Open Access Green
The ultrafine particle measurements in the Augsburger Umweltstudie, a panel study conducted in Augsburg, Germany, exhibit measurement error from various sources. Measurements of mobile devices show classical possibly individual-specific measurement error; Berkson-type error, which may also vary individually, occurs, if measurements of fixed monitoring stations are used. The combination of fixed site and individual exposure measurements results in a mixture of the two error types. We extended existing bias analysis approaches to linear mixed models with a complex error structure including individual-specific error components, autocorrelated errors, and a mixture of classical and Berkson error. Theoretical considerations and simulation results show, that autocorrelation may severely change the attenuation of the effect estimations. Furthermore, unbalanced designs and the inclusion of confounding variables influence the degree of attenuation. Bias correction with the method of moments using data with mixture measurement error partially yielded better results compared to the usage of incomplete data with classical error. Confidence intervals (CIs) based on the delta method achieved better coverage probabilities than those based on Bootstrap samples. Moreover, we present the application of these new methods to heart rate measurements within the Augsburger Umweltstudie: the corrected effect estimates were slightly higher than their naive equivalents. The substantial measurement error of ultrafine particle measurements has little impact on the results. The developed methodology is generally applicable to longitudinal data with measurement error.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Longitudinal Data Analysis ; Measurement Error ; Mixed Model ; Particulate Matter
ISSN (print) / ISBN 0323-3847
e-ISSN 1521-4036
Journal Biometrical Journal
Quellenangaben Volume: 60, Issue: 3, Pages: 480-497 Article Number: , Supplement: ,
Publisher Wiley
Publishing Place Weinheim
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Epidemiology II (EPI2)