The Gold Standard of Evidence
Meta-analysis sits at the top of the evidence hierarchy in medicine. By systematically combining results from multiple randomized controlled trials, it provides the most reliable estimate of a treatment's effect. The Cochrane Collaboration maintains thousands of meta-analyses covering interventions across all fields of medicine, and regulatory agencies like the FDA increasingly rely on meta-analytic evidence for approval decisions.
The Forest Plot
The visualization generates a forest plot — the canonical display for meta-analysis results. Each study appears as a row with a square (effect estimate, sized by weight) and horizontal line (confidence interval). Studies with larger samples and smaller variance receive more weight. The pooled estimate appears as a diamond at the bottom, synthesizing all available evidence into a single number with its confidence interval.
Fixed-Effect vs. Random-Effects Models
The simulation uses the DerSimonian-Laird random-effects model, which assumes that each study estimates a slightly different true effect due to variation in populations, interventions, and outcome measurements. This between-study variance τ² is estimated from the data and incorporated into the weights. When τ² = 0 (no heterogeneity), the random-effects model reduces to the fixed-effect model. As heterogeneity increases, the random-effects model gives more equal weights to studies, reducing the dominance of large studies.
Interpreting Heterogeneity
The I² statistic quantifies what percentage of total variation across studies is due to real differences rather than sampling error. Cochran's Q test provides a formal significance test, but with few studies it has low power (may miss real heterogeneity) and with many studies it may flag trivially small heterogeneity. When I² is high, the pooled estimate becomes less meaningful — the real scientific question shifts from 'what is the average effect?' to 'why do effects differ across studies?' — motivating subgroup analysis or meta-regression.