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 effect estimate is meaningless if the underlying studies are heterogeneous. The Higgins I² statistic (Higgins-Thompson 2002 Stat Med 21:1539) quantifies between-study variance; the conventional 25/50/75% interpretation tiers come from the Cochrane Handbook §10.10.2 (Higgins et al editors), NOT the original Higgins-Thompson paper which cautioned against rigid cutoffs. A meta-analysis with I² > 75% and a pooled effect must explain the heterogeneity (subgroup analysis, meta-regression) — pooling without explanation is statistically defensible but biologically unhelpful.