Implementing DerSimonian and Laird inverse variance method in Python

In this section, we will utilize the knowledge from the previous chapter and use the PythonMeta package, as well as the same coding principles and functions learned so far, to create an actual meta-analysis using the four RCTs identified during the data search.

Now to the coding part. Open your Python editor (Jupyter or Spyder). In parallel, make sure you have installed the PythonMeta package using the following command:

pip install PythonMeta

Then, once this is complete, you can import the PythonMeta package and give it a shorter name:

import PythonMeta as PMA

Then load the Data(), Meta(), and Fig() classes we will need later to load the data, conduct meta-analysis, and visualize the results:

# Load classes
data = PMA.Data()
model = PMA.Meta()
figure = PMA.Fig()

Now we need to define the settings of the meta-analysis as we learned in the previous chapter.

Since we are performing the meta-analysis...