| name | tooluniverse-meta-analysis |
| description | Meta-analysis / evidence synthesis — pool effect sizes across studies (odds ratios, risk ratios, hazard ratios, mean differences, correlations, GWAS betas) with fixed- or random-effects models, quantify heterogeneity (Q, I², τ²), and build a forest plot. Use when you have results from MULTIPLE studies and need a single pooled estimate, or to synthesize evidence from a systematic review / multiple GWAS / replicated experiments. Handles the error-prone effect-size + standard-error preparation (converting OR/HR/CI, two-group means±SD, proportions, and correlations into the (effect, SE) the pooling step needs). |
| disable-model-invocation | true |
Meta-Analysis & Evidence Synthesis
Pool quantitative results from multiple studies into one estimate, and judge how consistent the studies are. This is the statistical half of a systematic review (the literature-collection half is tooluniverse-literature-deep-research).
When to use this
- You have effect sizes from ≥2 studies/cohorts and want a single pooled estimate + CI.
- Synthesizing a systematic review, replicated experiments, multi-cohort GWAS, or multi-dataset associations.
- Deciding whether studies agree (low heterogeneity) or conflict (high heterogeneity).
Do NOT use it to find the studies — use tooluniverse-literature-deep-research / the literature tools for that, then bring the extracted numbers here. Before trusting any input study, consider checking it with Crossref_check_retraction.
The workflow
1. Extract (effect, SE) per study ← THE ERROR-PRONE STEP
2. Pick fixed vs random effects
3. Pool: MetaAnalysis_run
4. Read heterogeneity (I², Q, τ²)
5. Forest plot + interpret
Step 1 — Convert each study to (effect_size, se) ← do this carefully
The pooling step needs an effect size on an additive scale and its standard error. Ratio measures (OR/RR/HR) must be log-transformed first. Most reported numbers give you a CI, not an SE — derive the SE from the CI.
| What the paper reports | effect_size | se |
|---|
OR / RR / HR with 95% CI [L, U] | ln(point) | (ln(U) − ln(L)) / (2 × 1.96) |
| OR / RR / HR with a p-value (no CI) | ln(point) | ` |
GWAS / regression β with SE | β (as reported) | the reported SE |
| Two groups, means + SDs + n₁,n₂ | Hedges' g (see script) | SE of g (see script) |
Single proportion p, n | logit(p)=ln(p/(1−p)) | sqrt(1/(np) + 1/(n(1−p))) |
Pearson correlation r, n | Fisher z = atanh(r) | 1 / sqrt(n − 3) |
Critical rules
- Log-transform ratio measures. Pooling raw ORs is wrong; pool
ln(OR) and exponentiate the pooled result back. The script does this for you.
- One direction. Make sure every study's effect points the same way (e.g. "exposure increases risk"); flip sign / invert the ratio for studies coded the opposite way.
- Same effect measure. Don't mix OR with HR with mean-difference in one pool.
2 × 1.96 assumes a 95% CI; use 2 × 1.645 for 90%, 2 × 2.576 for 99%.
The helper script does these conversions — prefer it over hand math:
python skills/tooluniverse-meta-analysis/scripts/meta_analysis.py --input studies.csv
Step 2 — Fixed vs random effects
| Use fixed-effects when | Use random-effects when |
|---|
| Studies estimate the same true effect (e.g. exact replications, one trial split by site) | Studies differ in population/design/dose (the usual real-world case) |
| I² is low (<25%) | I² is moderate–high, or studies are clinically heterogeneous |
When unsure, report random-effects (DerSimonian–Laird) as primary — it is the conservative default and widens the CI to reflect between-study variance.
Step 3 — Pool with MetaAnalysis_run
tu run MetaAnalysis_run '{"method":"random","studies":[
{"name":"Smith 2019","effect_size":0.41,"se":0.12},
{"name":"Lee 2021","effect_size":0.67,"se":0.18},
{"name":"Garcia 2023","effect_size":0.33,"se":0.10}]}'
Returns pooled_effect, pooled_se, pooled_ci_lower/upper, pooled_z, pooled_p_value, a heterogeneity block (Q, Q_df, Q_p_value, I_squared, tau_squared), and per_study weights + CIs.
Scale foot-gun — read this. MetaAnalysis_run pools whatever scale you hand it and has no idea your inputs were ratios. For an OR/RR/HR you MUST pass the log-transformed effect_size + se from Step 1 (e.g. ln(1.42)=0.351, not 1.42) — feeding raw ratios silently produces a wrong pooled value with no error. And the values it returns — including its prose interpretation string — are on that same log scale. So: ignore the tool's interpretation field for ratios, and exp() the pooled_effect and CI bounds back to the OR/RR/HR scale yourself before reporting. The helper script avoids all of this — it takes raw ORs, tracks the scale, and prints results already back-transformed.
Step 4 — Interpret heterogeneity (decides the story)
| I² | Heterogeneity | What it means |
|---|
| 0–25% | Low | Studies largely agree; fixed-effects is defensible |
| 25–50% | Moderate | Prefer random-effects; note the variability |
| 50–75% | Substantial | Random-effects; investigate sources (subgroup / meta-regression) |
| >75% | Considerable | Pooling may be inappropriate — explain why studies differ instead |
Q_p_value < 0.10 → statistically significant heterogeneity (Q is low-powered, so 0.10 not 0.05).tau_squared is the between-study variance on the effect scale; > 0 is what random-effects adds over fixed.- Borderline I² (≈25–50%) with a non-significant Q (
Q_p_value ≫ 0.10), especially with few studies: fixed and random-effects converge — report random-effects as primary and note that fixed-effects agrees. Don't agonize over the model choice when both give essentially the same pooled estimate.
Step 5 — Forest plot + report
The script prints a text forest plot (per-study effect, CI, weight%, and the pooled diamond). Report, in order:
- Pooled estimate + 95% CI + p (on the interpretable scale — exponentiate ratios back).
- Number of studies and total N.
- Heterogeneity: I² + Q p-value + the model you chose and why.
- Direction/consistency: do all studies point the same way?
Example: "Across 3 cohorts (N=4,210), the pooled OR was 1.51 (95% CI 1.33–1.72, p=3.4×10⁻⁶), random-effects. Heterogeneity was substantial (I²=55%, Q p=0.11), so the random-effects model is reported; all three studies showed the same direction of effect."
Honest limitations
- Garbage in, garbage out. Meta-analysis cannot fix biased primary studies; check input study quality (and retraction status via
Crossref_check_retraction) first.
- Publication bias. A pooled estimate from only published studies is likely inflated. With ≥10 studies, inspect a funnel plot / Egger's test (the script notes this); with <10, state that small-study bias cannot be assessed.
- Ecological / aggregation issues. Pooling study-level summaries is not the same as pooling individual patient data.
- Don't over-pool. With I²>75% and clinically different studies, a single number can mislead — describe the variation instead.
Related skills
tooluniverse-literature-deep-research — find and grade the studies to feed in.tooluniverse-statistical-modeling — single-study regression, Cox, ORs (see references/cox_regression.md for HR extraction).tooluniverse-gwas-study-explorer / tooluniverse-gwas-finemapping — GWAS-specific multi-cohort analysis.
