Journal
BIOMETRICS
Volume 65, Issue 2, Pages 361-368Publisher
WILEY-BLACKWELL PUBLISHING, INC
DOI: 10.1111/j.1541-0420.2008.01103.x
Keywords
Clustered binary response; Generalized linear mixed models; Random effects
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Generalized linear mixed models (GLMMs) are widely used in the analysis of clustered data. However, the validity of likelihood-based inference in such analyses can be greatly affected by the assumed model for the random effects. We propose a diagnostic method for random-effect model misspecification in GLMMs for clustered binary response. We provide a theoretical justification of the proposed method and investigate its finite sample performance via simulation. The proposed method is applied to data from a longitudinal respiratory infection study.
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