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Practical guidance for using multiple data sources in systematic reviews and meta-analyses (with examples from the
Evan Mayo-Wilson1, Tianjing Li1, Nicole Fusco1
1Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD, 21205, USA.
Research Synthesis Methods
|October 24, 2017
Summary
Systematic reviews can be improved by carefully selecting and linking multiple data sources. Following a prespecified protocol for data extraction and outcome assessment reduces bias and research waste.
Area of Science:
- Medical research methodology
- Evidence-based medicine
Background:
- Systematic reviews synthesize evidence from multiple studies.
- Trial data can be reported across various sources (e.g., journal articles, conference abstracts).
- Differences in data sources can influence systematic review outcomes.
Purpose of the Study:
- To provide practical guidance for effectively using multiple data sources in systematic reviews.
- To enhance the reliability and reduce bias in systematic review findings.
Main Methods:
- Developed recommendations based on the Multiple Data Sources in Systematic Reviews (MUDS) study experience and prior evidence.
- Proposed a six-point practical guidance framework for managing multiple data sources.
Main Results:
- Recommended specifying data sources beforehand.
- Advised linking individual trials across multiple sources using modified PRISMA flowcharts.
- Stressed following prespecified protocols for data extraction, outcome definition, and handling discrepancies.
- Emphasized identifying included data sources and assessing potential influence on results.
- Suggested data sharing to reduce bias and research waste.
Conclusions:
- Systematic use of multiple data sources, guided by a clear protocol, is crucial for robust systematic reviews.
- Adherence to the proposed guidance can improve the accuracy and efficiency of evidence synthesis.
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