New meta-analysis (MA) features in Stata 18: MA for prevalence and multilevel MA

Meta-analysis is a statistical technique for combining the results from several similar studies. Stata’s meta command offers full support for meta-analysis from computing various effect sizes and producing basic meta-analytic summaries to performing tests for small-study effects. Stata 18 introduced support for meta-analysis of one proportion, meaning you can now use standard meta- analysis features such as forest plots and funnel plots with one-sample binary data. Stata 18 also introduced two new commands, meta meregress and meta multilevel, for performing multilevel meta-analysis. These commands allow you to analyze results from multiple studies in which the reported effect sizes are nested within higher-level groupings such as regions or schools. By properly accounting for the dependence among the effect sizes, we can produce more accurate inference. In this presentation, I will demonstrate how to perform meta-analysis of proportions and multilevel meta-analysis in Stata 18. I will provide a brief introduction to meta-analysis and discuss effect sizes and confidence intervals relevant to prevalence data. For multilevel data, we will see how to include random intercepts and coefficients at different levels of hierarchy, perform sensitivity analysis, and assess the variability among the effect sizes at different levels of the hierarchy.