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Related Experiment Videos

Meta-analysis by random effect modelling in generalized linear models.

M Aitkin1

  • 1Department of Statistics, University of Newcastle, Newcastle-upon Tyne, NE1 7RU, U.K.

Statistics in Medicine
|September 4, 1999
PubMed
Summary

This study advocates for the general use of random effect models in meta-analysis for multi-centre trials. It highlights the benefits of non-parametric maximum likelihood (NPML) analysis for these trials and similar epidemiological studies.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Trials

Background:

  • Meta-analysis of multi-centre trials commonly employs fixed or random effect models.
  • The choice of model impacts the interpretation and generalizability of results.
  • Administrative 'league table' analyses are prevalent in epidemiological research.

Purpose of the Study:

  • To advocate for the widespread adoption of random effect models in meta-analysis.
  • To demonstrate the utility of non-parametric maximum likelihood (NPML) analysis for multi-centre trials.
  • To unify NPML analysis with administrative 'league table' approaches.

Main Methods:

  • The study proposes and illustrates the application of non-parametric maximum likelihood (NPML) analysis.
  • NPML is applied to meta-analysis of multi-centre trials.

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  • The approach is extended to unify administrative 'league table' analyses.

    • Main Results:

    • Random effect models are argued as generally superior for meta-analysis.
    • Non-parametric maximum likelihood (NPML) analysis proves valuable for multi-centre trials.
    • The NPML approach successfully integrates 'league table' analyses.

    Conclusions:

    • The general use of random effect models is recommended for meta-analysis.
    • Non-parametric maximum likelihood (NPML) offers a robust analytical framework.
    • This unified approach enhances the analysis of multi-centre and epidemiological studies.