On fitting generalized linear mixed-effects models for binary responses using different statistical packages

The generalized linear mixed‐effects model (GLMM) is a popular paradigm to extend models for cross‐sectional data to a longitudinal setting. When applied to modeling binary responses, different software packages and even different procedures within a package may give quite different results. In this...

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Bibliographic Details
Published in:Statistics in medicine 2011-09, Vol.30 (20), p.2562-2572
Main Authors: Zhang, Hui, Lu, Naiji, Feng, Changyong, Thurston, Sally W., Xia, Yinglin, Zhu, Liang, Tu, Xin M.
Format: Article
Language:English
Subjects:
Computer Simulation
Cross-Sectional Studies - methods
Data Interpretation, Statistical
GLIMMIX
Humans
integral approximation
Linear Models
linearization
lme4
Longitudinal Studies
Medical statistics
NLMIXED
Reproducibility of Results
Respiratory Tract Diseases - therapy
SAS
Simulation
Software - standards
Software packages
ZELIG
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Summary:The generalized linear mixed‐effects model (GLMM) is a popular paradigm to extend models for cross‐sectional data to a longitudinal setting. When applied to modeling binary responses, different software packages and even different procedures within a package may give quite different results. In this report, we describe the statistical approaches that underlie these different procedures and discuss their strengths and weaknesses when applied to fit correlated binary responses. We then illustrate these considerations by applying these procedures implemented in some popular software packages to simulated and real study data. Our simulation results indicate a lack of reliability for most of the procedures considered, which carries significant implications for applying such popular software packages in practice. Copyright © 2011 John Wiley & Sons, Ltd.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.4265