Summary
In this chapter, we learned how to perform meta-analysis using a randomly created practice dataset and the PythonMeta package.
We learned how to create meta-analysis forest plots and funnel plots and how to interpret them. We also learned how to perform and interpret subgroup analysis, which is very important in biomedical research. We learned how to evaluate individual study effects, pooled effects, and publication bias.
Furthermore, we used statsmodels to implement meta-regression as a weighted linear regression model.
Finally, we learned how to plot different types of plots for the interpretation of meta-analysis results such as forest plots, funnel plots, and meta-regression bubble plots.
In the next chapter, we will be implementing what we learned in this chapter using a real-world meta-analysis with real-world publications.
