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Meta-analysis of published data using a linear mixed-effects model

D O Stram1

  • 1Department of Preventive Medicine, University of Southern California, Los Angeles, 90033, USA.

Biometrics
|June 1, 1996
PubMed
Summary
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This study introduces a linear mixed-effects regression model for meta-analysis, generalizing existing random-effects models. This framework enhances the analysis of published data for more robust scientific conclusions.

Area of Science:

  • Biostatistics
  • Medical Research Methodology

Background:

  • Traditional random-effects models are widely used in meta-analysis.
  • Existing models by DerSimonian and Laird (1986) and Begg and Pilote (1991) have limitations.
  • Meta-analysis synthesizes findings from multiple published studies.

Purpose of the Study:

  • To present a generalized framework for meta-analysis using a linear mixed-effects regression model.
  • To demonstrate the application of this new model in analyzing published data.
  • To extend the capabilities of current random-effects meta-analysis methods.

Main Methods:

  • Development of a linear mixed-effects regression model.
  • Generalization of established random-effects models.
  • Application of the model to example datasets from previous studies.

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Main Results:

  • The linear mixed-effects model provides a flexible framework for meta-analysis.
  • The proposed model encompasses and extends previous random-effects approaches.
  • Demonstrated utility through practical examples.

Conclusions:

  • Linear mixed-effects regression offers a powerful and adaptable approach to meta-analysis.
  • This method enhances the statistical rigor of synthesizing published research.
  • The generalized model improves upon existing random-effects meta-analysis techniques.