On fitting generalized linear mixed-effects models for binary responses using different statistical packages
Article first published online: 10 JUN 2011
Copyright © 2011 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 30, Issue 20, pages 2562–2572, 10 September 2011
How to Cite
Zhang, H., Lu, N., Feng, C., Thurston, S. W., Xia, Y., Zhu, L. and Tu, X. M. (2011), On fitting generalized linear mixed-effects models for binary responses using different statistical packages. Statist. Med., 30: 2562–2572. doi: 10.1002/sim.4265
- Issue published online: 17 AUG 2011
- Article first published online: 10 JUN 2011
- Manuscript Accepted: 21 MAR 2011
- Manuscript Received: 30 OCT 2009
- integral approximation;
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.