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

Hui Zhang*, Naiji Lu, Changyong Feng, Sally W. Thurston, Yinglin Xia, Liang Zhu, Xin M. Tu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

56 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)2562-2572
Number of pages11
JournalStatistics in Medicine
Volume30
Issue number20
DOIs
StatePublished - Sep 10 2011

Keywords

  • GLIMMIX
  • Integral approximation
  • Linearization
  • Lme4
  • NLMIXED
  • R
  • SAS
  • ZELIG

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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