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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
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A comparison of statistical methods for meta-analysis.

S E Brockwell1, I R Gordon

  • 1Department of Mathematics and Statistics, Richard Berry Building, The University of Melbourne, Victoria 3010, Australia.

Statistics in Medicine
|March 17, 2001
PubMed
Summary
This summary is machine-generated.

This meta-analysis reveals that common statistical methods for combining study results, particularly the DerSimonian and Laird method, often underestimate error in random effects models, especially with few studies.

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Area of Science:

  • Biostatistics
  • Clinical Research Methodology
  • Evidence-Based Medicine

Background:

  • Meta-analysis is crucial for synthesizing evidence from multiple studies.
  • Fixed and random effects models are standard statistical approaches for meta-analysis.
  • Accurate estimation of overall effects and confidence intervals is vital for reliable conclusions.

Purpose of the Study:

  • To evaluate three common estimation methods within a random effects model framework.
  • To compare the performance of these methods using confidence interval coverage probabilities.
  • To assess the accuracy of the DerSimonian and Laird method in meta-analysis.

Main Methods:

  • Application of three distinct random effects model estimation techniques.
  • Utilized a dataset of six studies investigating aspirin's effect post-myocardial infarction.
  • Comparison based on estimated coverage probabilities of confidence intervals for the overall treatment effect.

Main Results:

  • All considered meta-analysis techniques generally yielded confidence interval coverages below nominal levels.
  • The DerSimonian and Laird method demonstrated inadequate reflection of parameter estimation error.
  • This underestimation of error was particularly pronounced in meta-analyses with a small number of studies.

Conclusions:

  • Existing random effects model estimation methods, including DerSimonian and Laird, may provide overly narrow confidence intervals.
  • Researchers should exercise caution when interpreting meta-analysis results, especially with limited study numbers.
  • Further development of robust statistical methods for meta-analysis is warranted to improve accuracy in effect estimation.