Ma Meta Analysis

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"

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