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Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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

Hui Zhang1, Naiji Lu, Changyong Feng

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

Statistics in Medicine
|June 15, 2011
PubMed
Summary
This summary is machine-generated.

Generalized linear mixed-effects models (GLMMs) for binary data yield unreliable results across software. Researchers should carefully consider statistical approaches and software implementations for correlated binary responses.

Keywords:
GLIMMIXNLMIXEDRSASZELIGintegral approximationlinearizationlme4

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Generalized linear mixed-effects models (GLMMs) are widely used for longitudinal data.
  • Modeling correlated binary responses with GLMMs presents challenges.
  • Discrepancies exist in results from different software packages and procedures.

Purpose of the Study:

  • To describe and compare statistical approaches for fitting correlated binary responses using GLMMs.
  • To evaluate the performance of different GLMM procedures in popular software.
  • To highlight the implications of varying results for practical applications.

Main Methods:

  • Review of statistical methodologies underlying GLMM procedures for binary data.
  • Application of selected procedures from popular statistical software to simulated datasets.
  • Analysis of real-world study data using the same procedures.

Main Results:

  • Significant variability and lack of reliability were observed across most tested GLMM procedures.
  • Different software packages and internal procedures produce substantially different estimates.
  • The choice of statistical approach and software implementation critically impacts results.

Conclusions:

  • Most current GLMM procedures for correlated binary data lack reliability.
  • Researchers must exercise caution when applying GLMM software for binary outcomes.
  • Further investigation into robust statistical methods and software validation is warranted.