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Beyond the forest plot: The drapery plot.

Gerta Rücker1,2, Guido Schwarzer1

  • 1Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany.

Research Synthesis Methods
|April 27, 2020
PubMed
Summary
This summary is machine-generated.

The drapery plot visualizes the P-value function for meta-analysis results, offering a comprehensive alternative to traditional forest plots. This method enhances understanding during the reproducibility crisis.

Keywords:
P-valueP-value curvedrapery plotforest plotmeta-analysisreproducibility

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Area of Science:

  • Statistics
  • Biostatistics
  • Medical Research Methodology

Background:

  • The "reproducibility crisis" and "P-value controversy" necessitate novel statistical presentation methods.
  • The P-value function, proposed decades ago for single studies, offers a comprehensive data interpretation tool.
  • Existing meta-analysis visualization techniques, like forest plots, can become complex with numerous studies.

Purpose of the Study:

  • To introduce and illustrate the utility of the drapery plot for meta-analysis.
  • To present the P-value function for individual studies and pooled estimates within a meta-analysis.
  • To offer a visualization that complements or potentially replaces forest plots in complex meta-analyses.

Main Methods:

  • The study introduces the "drapery plot" to visualize the P-value function for meta-analyses.
  • The drapery plot displays P-value functions as curves for individual studies and pooled estimates.
  • A scaled variant of the plot, using the test statistic on the y-axis, is also presented.

Main Results:

  • The drapery plot effectively visualizes the P-value function for all components of a meta-analysis.
  • It simultaneously provides confidence intervals across all alpha levels and the prediction range for future studies.
  • The plot offers a complete visualization of pairwise meta-analysis data.

Conclusions:

  • The drapery plot is a valuable tool for interpreting meta-analysis results, especially in light of current statistical debates.
  • It serves as a complementary or alternative visualization to forest plots, particularly for large meta-analyses.
  • This method enhances the understanding of statistical significance and uncertainty in meta-analytic findings.