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Advice for improving the reproducibility of data extraction in meta-analysis
Edward R Ivimey-Cook1, Daniel W A Noble2, Shinichi Nakagawa3
1School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK.
Research Synthesis Methods
|August 12, 2023
Summary
Researchers can improve data extraction transparency in meta-analyses by following new guiding principles and utilizing R software packages like shinyDigitise and juicr for reproducible data policies.
Area of Science:
- Biostatistics
- Medical Research Methodology
Background:
- Data extraction is crucial for meta-analyses, yet its transparency and reproducibility often lag behind.
- Limited guidance exists for making the data extraction process more transparent and shareable.
Conclusions:
- Adopting the presented principles and tools can lead to more reproducible and transparent data extraction in meta-analyses.
- This enhances the overall integrity and trustworthiness of systematic reviews and meta-analytic findings.
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