As another alternative to the standard forest plot for visualizing many effect size estimates in a meta-analysis, one can use 'orchard plots' (Nakagawa et al., 2021). Example code to create such figures can be found here: https://www.metafor-project.org/doku.php/plots:forest_plot_orchard_style

#MetaAnalysis #RStats

In large meta-analyses, standard forest plot can become excessively large. One solution is to group the estimates together within studies. I just added an example to the metafor website illustrating this possibility:

https://www.metafor-project.org/doku.php/plots:forest_plot_with_grouped_estimates

#MetaAnalysis #RStats

🤔 Can you perform a meta-analysis of a single study?
💡 Yes, you can -- this makes perfect sense if you want to derive a "meta-analytic-predictive (MAP) prior" based on a previous study. And it is in fact done implicitly in certain shrinkage applications involving only 2 studies. @friede1 and I had a closer look at this case:
👉 https://doi.org/10.1017/rsm.2026.10081

#Bayesian #MetaAnalysis #MedStatGoe

#statstab #545 Dynamic Meta-analysis: When Transparency Meets Multiplicity

Thoughts: Seems like hard work but makes perfect sense. Combine this with live meta-analyses.

#metascience #metaanalysis #evidence #multiverse

https://drmattg.github.io/Uncertain_Ecologist/Dynamic_Meta_analysis.html

Dynamic Meta-analysis: When Transparency Meets Multiplicity

📊 Reliable evidence needs reliable methods.
Co-initiated by RC Trust PI Markus Pauly, the Göttingen symposium “Recent Advances on Statistical Methods for Meta-Analyses” brought together international experts to discuss how researchers can draw valid conclusions when only few studies or small samples are available.
🔗 https://rc-trust.ai/news/news-detail/reliable-evidence-needs-reliable-statistical-methods
#Statistics #MetaAnalysis #RCTrust #TUDortmund
Meta-analysis: Eating more legumes and soy is linked to lower risk of hypertension, itself a risk factor for cardiovascular disease & related mortality: doi.org/10.1136/bmjn... #Legumes #Beans #Lentils #Chickpeas #Peas #Soybeans #Tofu #Health #Hypertension #CVD #Review #MetaAnalysis

doi.org/10.1136/bmjnph...
Legume and soy consumption and the risk of hypertension: a systematic review and dose–response meta-analysis of prospective studies

Background Several studies have suggested that high intakes of legumes and soy products are associated with a lower risk of hypertension; however, the results have been inconsistent. We conducted a systematic review and meta-analysis to clarify the association between legumes and soy consumption and the risk of hypertension.Methods PubMed and Embase databases were searched up to 14 June 2025. Random effects models were used to calculate summary relative risks (RRs) and 95% confidence intervals (95% CIs) for the association between legume or soy consumption and hypertension risk. Heterogeneity was evaluated using I 2. The likelihood of causality was evaluated using World Cancer Research Fund (WCRF) criteria.Results 12 prospective studies were included in the meta-analysis. The summary RR for high versus low intake of legumes was 0.84 (95% CI 0.77 to 0.93, I 2=65%, pheterogeneity=0.003, n=10 studies, 86 098 cases, 309 853 participants) and for soy was 0.81 (95% CI 0.70 to 0.93, I2=82%, pheterogeneity<0.0001, n=7 studies, 93 934 cases, 278 200 participants). In the linear dose–response analyses, the summary RR per 100 g/day was 0.88 (95% CI 0.80 to 0.97, I2=69%, pheterogeneity=0.001, n=10) for legumes and 0.76 (95% CI 0.60 to 0.96, I2=89%, pheterogeneity<0.0001, n=6) for soy. The test for non-linearity was not significant for legumes (pnon-linearity=0.13), suggesting a linear reduction in risk up to ~170 g/day, while for soy there was indication of non-linearity (pnon-linearity=0.01), and most of the reduction in risk was observed up to an intake around 60–80 g/day. Although there was an indication of publication bias with Egger’s test (p=0.04) for legumes, this was explained by two outlying studies. Using WCRF criteria, the likelihood of causality was considered probable for both legumes and soy in relation to hypertension risk.Conclusion In this meta-analysis of 12 prospective cohort studies, legume and soy intakes were associated with lower risk of hypertension. These findings support dietary recommendations to increase the intake of legumes in the general population.

BMJ Nutrition, Prevention & Health

An update on the CRAN Task View on MetaAnalysis https://cran.r-project.org/view=MetaAnalysis which is still slowly growing. It now has more than 200 packages covering a wide range of situations.

#metaanalysis #Rstats

CRAN Task View: Meta-Analysis

This task view covers packages which include facilities for meta-analysis of summary statistics from primary studies. The task view does not consider the meta-analysis of individual participant data (IPD) which can be handled by any of the standard linear modelling functions but it does include some packages which offer special facilities for IPD.

It's been 84 years... okay, three months, but here's a PhD side quest: a mini meta-analysis.

Analysis & forest plot done in {metafor}, HTML report knitted in RMarkdown, {grateful} for software acknowledgements.

GitHub repo: https://github.com/ale-lazic/vacc_cvrg_meta/

HTML page preview: https://htmlpreview.github.io/?https://github.com/ale-lazic/vacc_cvrg_meta/blob/1d79d6506bc5dca5333fa05cbfb95953817e904b/vacc_cvrg_meta.html

#Rstats #Metaanalysis #Psychology

#statstab #531 Effect Size Calculator [Campbell]

Thoughts: A nice place for quick formulas for variance and effect sizes of various designs and data types.

#metaanalysis #effectsize #CohenD #calculator #Variance #Eta #effects

https://www.campbellcollaboration.org/calculator/equations

Supporting Information for: Fifty years later, and we still don’t know about badges of status