论文标题
荟萃分析参数计算：一种促进实验条件穿越的Python方法
Meta-analysis parameters computation: a Python approach to facilitate the crossing of experimental conditions
论文作者
论文摘要
荟萃分析是一种数据聚合方法，该方法根据几项研究的结果建立了总体和客观的证据水平。有必要在系统文献综述收集的数据中保持高水平的同质性。但是，目前的工具不允许对实验条件进行交叉引用，这可以解释研究之间观察到的异质性。本文旨在提出一项Python编程代码，该编程代码包含多个功能，允许对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对结果进行交叉检查。
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.
