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Living systematic reviews: 3. Statistical methods for updating meta-analyses.
Mark Simmonds1, Georgia Salanti2, Joanne McKenzie3
1Centre for Reviews and Dissemination, University of York, York YO10 5DD, UK.
Journal of Clinical Epidemiology
|September 16, 2017
Summary
Living systematic reviews (LSRs) require updated meta-analyses. This paper explores statistical methods like trial sequential analysis to manage accumulating evidence and avoid errors in living systematic reviews.
Area of Science:
- Medical Research Methodology
- Biostatistics
Background:
- Living systematic reviews (LSRs) necessitate continuous updates as new evidence emerges.
- Standard meta-analysis methods in LSRs can lead to increased Type I errors and inaccurate heterogeneity estimation with repeated updates.
Purpose of the Study:
- To address statistical challenges in updating meta-analyses within LSRs.
- To compare methods for managing accumulating evidence and maintaining statistical validity in LSRs.
Main Methods:
- The paper examines four statistical methods for updating meta-analyses: law of iterated logarithm, Shuster method, trial sequential analysis, and sequential meta-analysis.
- It compares their ability to control Type I and Type II errors and account for heterogeneity.
Main Results:
- Law of iterated logarithm and Shuster method primarily control Type I error inflation.
- Trial sequential analysis and sequential meta-analysis control both Type I and Type II errors, while also considering heterogeneity.
Conclusions:
- Specific statistical methods are crucial for maintaining the integrity of meta-analyses in LSRs.
- Trial sequential analysis and sequential meta-analysis offer robust solutions for managing accumulating data and heterogeneity in LSRs.
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