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. 2024 Jul 22;7(1):61.
doi: 10.5334/joc.389. eCollection 2024.

Breathing Life Into Meta-Analytic Methods

David Allbritton  1 , Pablo Gómez  2   3 , Bernhard Angele  4   5 , Martin Vasilev  6 , Manuel Perea  4   7
Affiliations

Breathing Life Into Meta-Analytic Methods

David Allbritton et al. J Cogn. .

Abstract

Meta-analyses have become indispensable in the behavioral sciences, combining and summarizing data from multiple studies. While they offer many advantages (e.g., increased power, higher generality, and resolving conflicting findings), they currently only provide a snapshot at a given point. In active research areas, frequent meta-analytic updates are necessary to incorporate new evidence. We propose guidelines for live, dynamic meta-analyses and introduce an accessible tool using the R environment. Our app, powered by the Shiny package, enables the meta-analyst to integrate evidence interactively as an update of an existing meta-analysis or from scratch (i.e., a new meta-analysis). By embracing dynamic meta-analyses and leveraging modern tools, researchers can ensure up-to-date meta-analyses in their respective fields.

Keywords: Bayesian statistics; app; meta-analysis; meta-analysis updating.

PubMed Disclaimer

Conflict of interest statement

The authors have no competing interests to declare.

Figures

The Input sidebar is on the left side, and the output is on the right as tabs
Figure 1
The Input sidebar is on the left side, and the output is on the right as tabs.
Options in the study criteria tab. The options within the top square are general for all analyses, and the ones in the bottom square are specific to the data file being used; in this case, it is for the updated Vasilev et al. meta-analysis
Figure 2
Options in the study criteria tab. The options within the top square are general for all analyses, and the ones in the bottom square are specific to the data file being used; in this case, it is for the updated Vasilev et al. meta-analysis.
Prior Specification tab
Figure 3
Prior Specification tab.
Outlier Check using the default data
Figure 4
Outlier Check using the default data.
Bayesian Forest Plot using the default dataset (check the Frequentist Forest Plot first when analyzing new data)
Figure 5
Bayesian Forest Plot using the default dataset (check the Frequentist Forest Plot first when analyzing new data).
Bayesian Additional Plots tab
Figure 6
Bayesian Additional Plots tab.
Output of the prior check. The data used in the example is a subset of the Vasilev et al. meta-analysis examining only children. Because there are only a few studies, the choice of priors has large consequences
Figure 7
Output of the prior check. The data used in the example is a subset of the Vasilev et al. meta-analysis examining only children. Because there are only a few studies, the choice of priors has large consequences.
Examples of posteriors with different selection criteria; this shows type of figure shows how an effect changes over publication period
Figure 8
Examples of posteriors with different selection criteria; this shows type of figure shows how an effect changes over publication period.
Adding a new data file
Figure 9
Adding a new data file.
Forest plot of the papers from before 2005 in the Maldonado dataset
Figure 10
Forest plot of the papers from before 2005 in the Maldonado dataset.
Forest plot of the papers from after 2005 in the Maldonado dataset and N>80
Figure 11
Forest plot of the papers from after 2005 in the Maldonado dataset and N > 80.
Forest plot of the lexical and reading-based studies in the Maldonado dataset
Figure 12
Forest plot of the lexical and reading-based studies in the Maldonado dataset.
See this image and copyright information in PMC

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