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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
Summary
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.
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.
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