Running and Reporting a Meta-analysis in R

The capstone. Set up a reproducible R project, run binary and continuous meta-analyses with sensitivity checks, then bring effect size, precision, heterogeneity and risk of bias together into an honest interpretation and a PRISMA-structured write-up you could submit.

Who this level is for

Learners completing the review journey who want a finished, reportable meta-analysis, and anyone who must interpret pooled evidence for a paper, guideline or policy brief. Recommended after Levels 1-4.

What you'll learn

• How to set up an R project for meta-analysis and import your extracted data (using meta or metafor)
• How to run fixed- and random-effects meta-analysis for a binary outcome and produce a forest plot
• How to meta-analyse a continuous outcome and run basic sensitivity checks (for example, excluding high risk-of-bias studies)
• How to interpret results by combining effect, precision, heterogeneity and risk of bias, and what "no effect" versus "imprecise evidence" really means
• The PRISMA checklist, and how the Methods and Results map to the review process
• How to write the Methods and Results sections of a systematic review

You finish with: A forest plot + a written interpretation and a PRISMA-structured Methods and Results draft.