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

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arxiv 2007.07799 v1 pith:3UQZ5MVZ submitted 2020-07-13 stat.ME cs.MSstat.AP

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

Flavien Quijoux , Charles Truong , Ali\'enor Vienne-Jumeau , Laurent Oudre , Fran\c{c}ois BERTIN-HUGAULT , Philippe ZAWIEJA , Marie LEFEVRE , Pierre-Paul VIDAL
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Damien RICARD
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classification stat.ME cs.MSstat.AP
keywords dataexperimentalstudiesaggregationallowingconditionslevelmeta-analysis
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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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