Prevalence Meta-analysis (Video 4: Heterogeneity, Bias, and Reporting)

In this final segment of the DeepDive, Dr. Ibraheem Abioye walks participants through the advanced components of conducting and interpreting a prevalence meta-analysis. Covering slides 58–94, this session builds on the statistical and applied foundations from prior videos and focuses on understanding heterogeneity, evaluating robustness, and reporting results with transparency.

Topics covered include:
Understanding heterogeneity
Why heterogeneity is expected in prevalence meta-analyses
Interpreting Q, I², and τ² statistics
Using prediction intervals to understand the expected range of future study estimates

Subgroup Analysis & Meta-regression
When and how to compare pooled prevalence across subgroups
Conducting meta-regression to evaluate study-level moderators
Interpreting regression coefficients, predicted prevalence, and residual heterogeneity

⚠️ Publication Bias in Prevalence Data
Why funnel plots often fail in prevalence meta-analysis
Limitations of Egger’s test when heterogeneity is high
Conceptual pitfalls and what to (and not to) conclude

🛠️ Sensitivity & Influence Analyses
Leave-one-out (influence) analysis to identify outlier studies
Baujat plots to visualize study contribution to heterogeneity
Cumulative meta-analysis to evaluate time trends and early-study effects

📝 Reporting Guidelines
Best practices for reporting systematic reviews using PRISMA
Applying the GRADE framework to evaluate certainty of evidence
Essential elements for transparent, reproducible prevalence meta-analysis reporting

By the end of this session, viewers will understand how to interpret, validate, and communicate the results of a prevalence meta-analysis with scientific rigor.