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On random-effects meta-analysis.

D Zeng1, D Y Lin1

  • 1Department of Biostatistics, CB #7420, University of North Carolina, Chapel Hill, North Carolina 27599, U.S.A.

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|December 22, 2015
PubMed
Summary
This summary is machine-generated.

Meta-analysis uses random-effects models to combine study results. A new method shows the standard meta-analysis estimator is more efficient than joint maximum likelihood, especially with few studies.

Keywords:
Clustered dataEvidence-based medicineGenetic associationHeterogeneityIndividual patient dataMaximum likelihood estimationRandom-effects modelResearch synthesisSummary statistic

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Area of Science:

  • Biostatistics
  • Statistical Modeling

Background:

  • Meta-analysis is crucial for synthesizing research findings.
  • Random-effects models are commonly used to address between-study heterogeneity.
  • Current asymptotic approximations for meta-analysis estimators can be inaccurate with a small number of studies.

Purpose of the Study:

  • To establish the asymptotic properties of meta-analysis and joint maximum likelihood estimators.
  • To compare the efficiency of these two estimation methods.
  • To develop improved statistical inference techniques for meta-analysis.

Main Methods:

  • Asymptotic analysis of meta-analysis and joint maximum likelihood estimators.
  • Comparison of estimator efficiencies under varying numbers of studies.
  • Development and application of a novel resampling technique for statistical inference.

Main Results:

  • The standard meta-analysis estimator is at least as efficient as the joint maximum likelihood estimator.
  • The commonly used asymptotic normal approximation for meta-analysis estimators is inaccurate for small numbers of studies.
  • The proposed resampling technique enhances the accuracy of statistical inference.

Conclusions:

  • The standard meta-analysis approach offers superior or equal efficiency compared to joint maximum likelihood estimation.
  • Accurate statistical inference in meta-analysis, particularly with limited studies, can be improved with novel techniques.
  • The findings have implications for the reliable synthesis of evidence in research.