Methodology notes, from our own team.
Practical, specific guidance from our methodologists and consultants — not general advice you could find anywhere.
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Subgroup Analysis in Meta-Analysis: When and How to Use It
Subgroup analysis can genuinely explain heterogeneity or mislead readers with a spurious, underpowered comparison -- the difference comes down almost entirely to whether it was pre-specified.
Aug 2026 · 5 min read · By Dr. Samuel Osei
Network Meta-Analysis: A Beginner’s Guide
Network meta-analysis lets you compare interventions that were never tested head-to-head in a single trial, but that power comes with assumptions a standard meta-analysis does not require.
Aug 2026 · 5 min read · By Dr. Samuel Osei
Effect Size Types in Meta-Analysis Explained
A plain-language explanation of the main effect size types used in meta-analysis, including odds ratio, risk ratio, and mean difference, and when to use each.
Aug 2026 · 3 min read · By Dr. Samuel Osei
SUCRA in Network Meta-Analysis: What It Means and How to Report It
A clear explanation of SUCRA scores in network meta-analysis, what they do and don't tell you, and how to report them correctly.
Aug 2026 · 3 min read · By Dr. Samuel Osei
Trial Sequential Analysis Explained: When and Why to Use It
What trial sequential analysis is, when it strengthens a meta-analysis, and how to interpret the output correctly.
Aug 2026 · 3 min read · By Dr. Samuel Osei
How to Read (and Build) a Forest Plot
A plain-language guide to reading a forest plot, choosing the right pooling model, and avoiding the most common meta-analysis mistakes.
Aug 2026 · 3 min read · By Dr. Samuel Osei
Publication Bias in Meta-Analysis: How to Detect and Address It
Studies with null or unfavorable results are less likely to get published at all, which means your pooled estimate may be systematically inflated. Here is how to actually check for it.
Aug 2026 · 5 min read · By Dr. Samuel Osei
Statistical Power in Meta-Analysis: Why Small Reviews Struggle
Pooling studies together is often assumed to solve underpowered individual trials automatically. It helps, but not as much or as reliably as people expect.
Aug 2026 · 5 min read · By Dr. Samuel Osei
How to Interpret a Forest Plot (A Step-by-Step Guide)
A forest plot packs an entire meta-analysis into one image, but only if you know what each element represents. Here is how to actually read one.
Aug 2026 · 5 min read · By Dr. Samuel Osei
Fixed-Effect vs. Random-Effects Meta-Analysis: How to Choose
The choice between fixed-effect and random-effects models is not a style preference. It rests on a specific, testable assumption about your included studies -- and getting it wrong is one of the fastest ways to draw a statistical reviewer objection.
Aug 2026 · 5 min read · By Dr. Samuel Osei
SUCRA Explained: Interpreting Network Meta-Analysis Rankings Correctly
A high SUCRA score looks like a clear, confident ranking, but the underlying uncertainty is easy to miss unless you check the actual effect estimates behind it.
Jul 2026 · 5 min read · By Dr. Samuel Osei
Egger’s Test and Funnel Plot Asymmetry, Explained
Egger’s test gives a formal number for something a funnel plot only shows visually, but it carries specific statistical limitations worth understanding before you cite it.
Jul 2026 · 5 min read · By Dr. Samuel Osei
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