Meta-analysis using Python: a hands-on tutorial.

Area of Science:

• Biostatistics
• Computational Statistics
• Evidence-Based Medicine

Background:

• Meta-analysis is crucial for generating high-quality evidence, particularly in quantitative research.
• Open-source software for meta-analysis is gaining traction due to its accessibility and flexibility.
• This paper addresses the need for practical guidance on performing meta-analysis using Python.

Purpose of the Study:

• To provide step-by-step instructions for conducting meta-analysis using Python.
• To share Python code for generating standard meta-analytic outputs.
• To assess the feasibility and accuracy of Python for meta-analysis.

Main Methods:

• Utilized the PythonMeta package for meta-analysis on an open-access dataset.
• Employed Python's zEpid package for creating forest plots.
• Developed Python scripts for contour-enhanced funnel plots to assess asymmetry.
• Cross-validated results with analyses performed in R and STATA.

Main Results:

• Successfully generated standard meta-analytic outputs using Python.
• Python code and instructions were provided for replication.
• Results obtained via Python were consistent with those from R and STATA.

Conclusions:

• Python is a capable and flexible tool for performing meta-analysis.
• The provided Python scripts enable researchers to generate reliable meta-analytic results.
• Python offers potential for further enhancements in meta-analysis workflows.