The meta esize command performs meta-analysis of two-sample binary or continuous data. Now, it also performs meta-analysis of one-sample binary data, also known as meta-analysis of proportions or meta-analysis of prevalence.
These types of data commonly appear in meta-analysis studies when pooling results from studies that each estimate one proportion. For instance, you may have studies reporting the prevalence of a particular disease or the proportion of students who drop out of high school. In this setting, effect sizes such as Freeman–Tukey-transformed proportions or logit-transformed proportions are typically used in the meta-analysis.
Afier meta esize, use other commands in the meta suite for further analysis. For instance, create a forest plot with meta forestplot, perform subgroup analysis by adding the subgroup() option to meta forestplot, summarize meta-analysis data with meta summarize, or construct a funnel plot with meta funnelplot.