Meta-analysis & Forest Plot
Pool the results of several independent studies. Supports binary outcomes (OR odds ratio) and continuous outcomes (MD mean difference), provides fixed-effect and random-effects (DerSimonian-Laird) models, computes heterogeneity (I², Q, τ²), and draws a forest plot.
① Choose data type and model
② Enter the study data
One study per row: study name + treatment-group events + treatment-group non-events + control-group events + control-group non-events (the 2×2 cells a b c d), Tab/comma-separated.
How to use & methodology
What is meta-analysis for?
Meta-analysis quantitatively pools the results of several independent studies on the same question into a combined effect estimate and assesses consistency between studies. It is one of the highest-level evidence types in evidence-based medicine, and the forest plot is its standard presentation.
How do I choose between fixed and random effects?
Fixed effect assumes all studies estimate the same true effect with only sampling error; random effects assume the true effects themselves differ between studies. Choose the model in advance from the study design and inferential goal (e.g. random effects when populations and interventions differ materially and you want to generalise), not from I² or the Q test.
How do I interpret I²?
I² is the percentage of total variation due to between-study variation: 0–40% heterogeneity may be unimportant, 30–60% moderate, 50–90% substantial, 75–100% considerable. The higher I², the less consistent the results and the more cautiously you should pool and explore causes (population, dose, design differences).
How do I read the forest plot?
Each study is a square (effect size, square size = weight) with a horizontal line (95% CI); the diamond at the bottom is the pooled effect, its width being its CI. The central dashed vertical line is the null-effect line (OR=1 or MD=0); a line or diamond crossing it means that result is not statistically significant.
Paste the link in Slack, Teams, X, or LinkedIn — the preview image comes from this page’s Open Graph card.