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Meta-analysis is a common approach for combining results (effect sizes) from multiple previous studies. But what if you are interested in more than one effect size from each study? For example, multiple trials have been designed to compare the effects of different types of exercise on systolic blood pressure. Thus, each study has an effect size for each type of exercise. Researchers may want to pool these study-specific effect sizes together. However, it is critical that you account for the correlation between the multiple effect sizes reported by each individual study. Multivariate meta-analysis allows you to perform meta-analysis with this type of data, while accounting for the within-study correlation.
With Stata's meta mvregress command, you can perform multivariate meta-analysis and meta-regression. You can fit random-effects and fixed-effects models, apply adjustments to the standard errors, and perform sensitivity analysis. Additionally, you can compute multivariate heterogeneity statistics, obtain fitted values, and more after fitting your model.
Join us as we show you how to perform multivariate meta-analysis in Stata. Find out how you can assess heterogeneity jointly for a subset of dependent variables and assess model fit.
The webinar is free, but you must register to attend. Registrations are limited so register soon.
We will send you an email prior to the start of the course with instructions on how to access the webinar.
Gabriela Ortiz is an Applied Econometrician at StataCorp. She holds a bachelor's degree in psychology from the University of California, Davis, and a master's degree in economics from California State University, Long Beach. Gabriela is a primary author of Stata's reporting manual and has contributed to the development of each of the reporting features. She also developed and regularly teaches Stata's introductory webinars.