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Stat Med. 2011 Sep 10;30(20):2562-72. doi: 10.1002/sim.4265. Epub 2011 Jun 10.

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

Author information

1
Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, U.S.A.. hui.zhang@stjude.org.

Abstract

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.

KEYWORDS:

GLIMMIX; NLMIXED; R; SAS; ZELIG; integral approximation; linearization; lme4

PMID:
21671252
PMCID:
PMC3175267
DOI:
10.1002/sim.4265
[Indexed for MEDLINE]
Free PMC Article

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