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How trace plots help interpret meta-analysis results.

Christian Röver1, David Rindskopf2, Tim Friede1

  • 1Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.

Research Synthesis Methods
|December 15, 2023
PubMed
Summary
This summary is machine-generated.

The trace plot, rarely used in meta-analysis, visualizes sensitivity to the between-study standard deviation. This informative plot helps assess plausible values for meta-analysis and meta-regression, enhancing statistical interpretation.

Keywords:
best linear unbiased prediction (BLUP)meta‐analysisrandom‐effects modelshrinkage

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Area of Science:

  • Statistics
  • Biostatistics
  • Quantitative Research Methods

Background:

  • Trace plots are underutilized in meta-analysis despite their informational value.
  • Understanding the sensitivity of meta-analysis results to the between-study standard deviation is crucial.

Purpose of the Study:

  • To define, illustrate, and emphasize the importance of the trace plot in meta-analysis.
  • To demonstrate how trace plots aid in visualizing sensitivity to the between-study standard deviation.

Main Methods:

  • The Bayesian trace plot integrates posterior density, between-study standard deviation, and shrunken effect estimates.
  • Comparable frequentist and empirical Bayes versions are discussed.
  • Illustrations use meta-analysis and meta-regression examples, with R package implementations (bayesmeta, metafor).

Main Results:

  • Trace plots reveal sensitivity to the between-study standard deviation, especially when precision is limited.
  • They visually distinguish plausible from implausible values of this parameter.
  • The method enhances the interpretation of parameter and shrunken study effect estimates.

Conclusions:

  • The trace plot is a valuable, yet underused, tool for assessing the impact of the between-study standard deviation in meta-analysis.
  • It improves the robustness and transparency of meta-analytic findings.
  • Implementation is accessible in R for both Bayesian and frequentist approaches.