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.

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Meta 分析参数计算：一种促进实验条件交叉的 Python 方法

Meta 分析是一种数据聚合方法，它根据多项研究的结果建立整体和客观的证据水平。从系统的文献综述中收集的数据的聚合必须保持高度的同质性。然而，目前的工具不允许交叉引用可以解释研究之间观察到的异质性的实验条件。本文旨在提出一个包含多个函数的 Python 编程代码，允许对来自许多研究的数据进行分析和快速可视化，同时允许通过实验条件交叉检查结果。