Meta-analysis parameters computation: a Python approach to facilitate the crossing of experimental conditions

TL;DR

This paper introduces a Python-based tool for meta-analysis that simplifies data aggregation, visualization, and cross-referencing of experimental conditions to better understand heterogeneity across studies.

Contribution

It provides a set of Python functions enabling efficient analysis and visualization of meta-analysis data with cross-checking of experimental conditions.

Findings

Facilitates rapid visualization of meta-analysis data

Enables cross-referencing of experimental conditions

Improves understanding of heterogeneity in meta-analyses

Abstract

Meta-analysis is a data aggregation method that establishes an overall and objective level of evidence based on the results of several studies. It is necessary to maintain a high level of homogeneity in the aggregation of data collected from a systematic literature review. However, the current tools do not allow a cross-referencing of the experimental conditions that could explain the heterogeneity observed between studies. This article aims at proposing a Python programming code containing several functions allowing the analysis and rapid visualization of data from many studies, while allowing the possibility of cross-checking the results by experimental condition.