doing-meta-analysis-r

name	doing-meta-analysis-r
domain	research
version	1.0.0
license	CC0-1.0
language	R
description	R-native meta-analysis via Harrer et al. (2021) — effect sizes, pooling, forest/funnel plots, heterogeneity, subgroup, meta-regression, publication bias (Egger, trim-and-fill, PET-PEESE, p-curve), NMA, Bayesian MA, SEM-MA, RoB plots, power analysis.
triggers	["meta-analysis","forest plot","heterogeneity","I-squared","tau-squared","Egger","trim and fill","meta-regression","network meta-analysis","Harrer","dmetar","metafor","systematic review pooling","effect size pooling"]
sources	["https://github.com/MathiasHarrer/Doing-Meta-Analysis-in-R","https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/"]
citation	Harrer, M., Cuijpers, P., Furukawa, T.A., & Ebert, D.D. (2021). Doing Meta-Analysis with R: A Hands-On Guide. Chapman & Hall/CRC Press. ISBN 978-0-367-61007-4.

Doing Meta-Analysis in R

Canonical reference: Harrer et al. (2021) Doing Meta-Analysis with R: A Hands-On Guide.

When to use

Invoke when the user asks for any of: effect-size pooling, forest/funnel plots, heterogeneity diagnostics, subgroup / meta-regression, publication-bias assessment (Egger, trim-and-fill, PET-PEESE, p-curve), network meta-analysis, Bayesian meta-analysis, SEM-MA, RoB plots, or power analysis.

Required R packages

install.packages(c("meta", "metafor", "netmeta", "brms"))
# dmetar is a companion package — bundled with the Harrer book
remotes::install_github("MathiasHarrer/dmetar")

Canonical pipeline

1. Import — esc / metafor::escalc() to compute effect sizes (SMD, OR, HR, RR).
2. Pool — meta::metagen() (generic IV), metabin(), metacont(), metacor(). Default: REML random-effects; report τ², I², prediction interval.
3. Visualize — meta::forest(), funnel(); save to PDF/PNG at 300 DPI.
4. Diagnose — subgroup via update.meta(subgroup=), meta-regression via metafor::rma().
5. Publication bias — metabias() (Egger), trimfill(), PET-PEESE (metafor), p-curve (dmetar::pcurve()).
6. NMA (optional) — netmeta::netmeta(), SUCRA via netrank().
7. Bayesian (optional) — brms::brm(bf(yi | se(sei) ~ 1 + (1|study))).
8. Report — PRISMA 2020 flow diagram via companion skill prisma2020-flow-diagram.

IPAI integration

• Primary use case: PrismaLab R&D systematic reviews (BIR entity -00002), JBLMGH Neurology manuscripts (CAVE score PSE, toxoplasmosis, HIV-TB co-infection).
• Artifact storage: write final RData + figures to Azure Blob Storage (stipaidevlake/research/<study_id>/) and register metadata in ops.artifacts.
• Companion skills: meta-analysis-pipeline (orchestration), prisma2020-flow-diagram (reporting).

Safety

• Never invent effect sizes — require a study-level CSV with fields: study_id, n1, n2, mean1, mean2, sd1, sd2 (continuous) or study_id, events1, n1, events2, n2 (binary).
• Flag I² > 75% in output and recommend subgroup / meta-regression.
• Never suppress studies from pooling without an explicit, logged reason.