Prevalence Meta-analysis (Video 4: Heterogeneity, Bias, and Reporting) скачать в хорошем качестве

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.