bio-data-visualization-forest-funnel-plots
Build forest plots (HR, OR, RR, beta-coefficient summaries with CIs) and funnel plots (meta-analysis publication-bias diagnostics) using forestplot, metafor, ggforest, and MendelianRandomization with proper axis-scaling, summary-diamond placement, subgroup nesting, and Egger / trim-and-fill asymmetry tests. Use when summarizing effects across subgroups, trials, or instruments — meta-analysis, Mendelian randomization, subgroup HRs.
npx skills add BioTender-max/awesome-bio-agent-skills --skill forest-funnel-plots --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: metafor 4.4+, forestplot 3.1+, ggforestplot 0.1+ (subgroup forests), ggforest from survminer 0.4.9+, MendelianRandomization 0.10+. Before using code patterns, verify installed versions match. If versions differ: - R: `packageVersion('<pkg>')` then `?function_name` If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Forest and Funnel Plots **"Summarize effects across studies / subgroups"** -> Render each effect estimate (HR, OR, RR, β) as a square (size = inverse variance / weight), horizontal bar (95% CI), and label, with an optional summary diamond at the bottom from a meta-analysis pool (fixed-effect or random-effects). The funnel plot diagnoses publication bias by plotting effect size vs precision; asymmetry indicates missing small-study-with-null-result publications (Egger 1997). - R: `metafor::forest`, `metafor::funnel`, `forestplot::forestplot`, `survminer::ggforest` (Cox HR forests), `MendelianRandomization::mr_forest` ## The Single Most Important Modern Insight -- Heterogeneity Is the First Question A pooled ef
- Version Compatibility
- The Single Most Important Modern Insight -- Heterogeneity Is the First Question
- Decision Tree by Analysis Type
- Fixed-Effect vs Random-Effects Meta-Analysis
- metafor::rma + forest -- The Reference Implementation
- ggforest for Cox Subgroup Forests
- Small-k Regime -- When Meta-Analysis Asymptotics Break
- Funnel Plot and Egger Test
- Per-Method Failure Modes
- Pooling under high heterogeneity without explanation
- Fixed-effect when studies are heterogeneous
- Egger test p-value over-interpreted
- Trim-and-fill imputed studies presented as data
- Subgroup forest without interaction test
What does the bio-data-visualization-forest-funnel-plots skill do?
Build forest plots (HR, OR, RR, beta-coefficient summaries with CIs) and funnel plots (meta-analysis publication-bias diagnostics) using forestplot, metafor, ggforest, and MendelianRandomization with proper axis-scaling, summary-diamond placement, subgroup nesting, and Egger / trim-and-fill asymmetry tests. Use when summarizing effects across subgroups, trials, or instruments — meta-analysis, Mendelian randomization, subgroup HRs.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill forest-funnel-plots --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.