ma-meta-analysis

name	ma-meta-analysis
description	Run statistical meta-analysis in R with renv, generate effect estimates, heterogeneity, and publication bias diagnostics, and export figures and tables. Use when analyzing extracted study data.

Ma Meta Analysis

Overview

Analyze extracted data using standard meta-analysis methods and produce validated outputs.

Inputs

• 05_extraction/extraction.csv
• 05_extraction/data-dictionary.md

Outputs

• 06_analysis/01_setup.R
• 06_analysis/02_effect_sizes.R
• 06_analysis/03_models.R
• 06_analysis/04_subgroups_meta_regression.R
• 06_analysis/05_plots.R
• 06_analysis/06_tables.R
• 06_analysis/07_sensitivity.R
• 06_analysis/08_bias.R
• 06_analysis/09_validation.R
• 06_analysis/07_export_tables.R
• 06_analysis/figures/ PNG files at 300 dpi
• 06_analysis/tables/ PNG + HTML + DOCX + CSV tables (via gt + flextable)
• 06_analysis/validation.md
• 06_analysis/renv.lock

Statistical Defaults

All models must use REML + Hartung-Knapp (Cochrane mandate, July 2025):

# meta:    metagen(..., method.tau = "REML", hakn = TRUE)
# metafor: rma(..., method = "REML", test = "knha")

Run DerSimonian-Laird as sensitivity analysis for comparison, but REML + HKSJ is the primary result.

Workflow

1. Initialize renv in 06_analysis/ and record package versions.
   • Run renv::init() in 06_analysis/
   • Creates 06_analysis/renv.lock
2. Copy the R templates from assets/r/ into 06_analysis/ and adapt them to the study schema.
   • Copy assets/r/01_setup.R → 06_analysis/01_setup.R
   • Copy templates 02-09 similarly
3. Compute effect sizes with metafor::escalc for the outcome type.
   • In 06_analysis/02_effect_sizes.R (use metafor::escalc() function)
4. Fit primary models using meta and/or metafor with REML + Hartung-Knapp defaults.
   • In 06_analysis/03_models.R (L10-30: metagen(..., method.tau = "REML", hakn = TRUE))
5. Assess heterogeneity (I2, Q, tau2), subgroup analyses, and meta-regression when applicable.
   • In 06_analysis/04_subgroups_meta_regression.R
6. Conduct sensitivity analyses and publication bias diagnostics.
   • In 06_analysis/07_sensitivity.R
   • In 06_analysis/08_bias.R
7. Generate forest and funnel plots at 300 dpi.
   • In 06_analysis/05_plots.R
   • Write to 06_analysis/figures/*.png (png(..., res=300, width=3000, height=2400))
8. Use gtsummary to build manuscript-ready summary tables.
   • In 06_analysis/06_tables.R (use gtsummary::tbl_summary())
9. Export tables as PNG/HTML/DOCX via gt + flextable for manuscript sync.
   • In 06_analysis/07_export_tables.R
   • Write to 06_analysis/tables/*.png, 06_analysis/tables/*.html, 06_analysis/tables/*.docx
10. Summarize key results and decisions in 06_analysis/validation.md.
    • Write to 06_analysis/validation.md

Resources

• assets/r/ provides a scaffolded R workflow that maps to the standard steps.
• references/r-meta-roadmap.md summarizes the expected analysis tasks.

Validation

• Reproduce key estimates from at least one study subset.
• Confirm that effect sizes match the extraction units and directionality.

Stage Exit: Stamp the artifact

Record provenance for the primary analysis outputs. See artifact-stamping.md.

uv run tooling/python/session_log.py --project <project-name> append \
  --stage 06_analysis \
  --artifact 06_analysis/results.rds \
  --generator ai \
  --notes "forestplot.png, league_table.csv generated from this run"

Pipeline Navigation

Step	Skill	Stage
Prev	/ma-data-extraction	05 Data Extraction
Next	/ma-manuscript-quarto	07 Manuscript Drafting
All	/ma-end-to-end	Full pipeline orchestration