Advice for improving the reproducibility of data extraction in meta-analysis.
data extraction
juicr
meta-analysis
metaDigitise
reproducibility
shinyDigitise
Journal
Research synthesis methods
ISSN: 1759-2887
Titre abrégé: Res Synth Methods
Pays: England
ID NLM: 101543738
Informations de publication
Date de publication:
Nov 2023
Nov 2023
Historique:
revised:
26
07
2023
received:
17
03
2023
accepted:
27
07
2023
medline:
8
11
2023
pubmed:
12
8
2023
entrez:
12
8
2023
Statut:
ppublish
Résumé
Extracting data from studies is the norm in meta-analyses, enabling researchers to generate effect sizes when raw data are otherwise not available. While there has been a general push for increased reproducibility in meta-analysis, the transparency and reproducibility of the data extraction phase is still lagging behind. Unfortunately, there is little guidance of how to make this process more transparent and shareable. To address this, we provide several steps to help increase the reproducibility of data extraction in meta-analysis. We also provide suggestions of R software that can further help with reproducible data policies: the shinyDigitise and juicr packages. Adopting the guiding principles listed here and using the appropriate software will provide a more transparent form of data extraction in meta-analyses.
Types de publication
Meta-Analysis
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
911-915Informations de copyright
© 2023 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.
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