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Comparison of bias adjustment in meta-analysis using data-based and opinion-based methods
Jennifer C Stone1, Luis Furuya-Kanamori2, Edoardo Aromataris1
1JBI, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia.
JBI Evidence Synthesis
|February 27, 2024
Summary
Comparing bias adjustment methods in meta-analysis, this study found opinion-based approaches add uncertainty. Data-based and quality effects methods offered minor bias adjustments, aligning with inverse variance heterogeneity models.
Area of Science:
- Biostatistics
- Meta-analysis methodology
Background:
- Existing meta-analysis bias adjustment methods lack comprehensive comparison with unadjusted approaches.
- This study addresses this gap by evaluating six bias adjustment techniques against two unadjusted models.
Approach:
- Re-analyzed a meta-analysis of 10 randomized controlled trials.
- Employed two data-based methods (Welton's, Doi's quality effects model) and four opinion-informed methods.
- Compared results against DerSimonian-Laird random effects and Doi's inverse variance heterogeneity models.
Key Points:
- Opinion-based methods increased uncertainty in random effects model estimates.
- Data-based and quality effects methods yielded distinct results.
- These methods showed alignment with the inverse variance heterogeneity model, with slight downward bias adjustment.
Conclusions:
- Opinion-based methods appear to introduce uncertainty rather than effectively adjust for bias.
- Data-based methods provide a more nuanced approach to bias adjustment in meta-analysis.
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