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Methods to calculate uncertainty in the estimated overall effect size from a random-effects meta-analysis
Areti Angeliki Veroniki1,2, Dan Jackson3, Ralf Bender4
1Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada.
Research Synthesis Methods
|August 22, 2018
Summary
This study reviews random-effects meta-analysis methods for estimating overall effect size. It compares various approaches for calculating point estimates, confidence intervals, and prediction intervals, highlighting preferred methods based on simulation and real-world data.
Area of Science:
- Biostatistics
- Medical Research Methodology
Background:
- Meta-analyses are crucial for synthesizing evidence in systematic reviews.
- Random-effects models are preferred when study heterogeneity is present.
- The DerSimonian and Laird method, a common random-effects approach, has known statistical limitations.
Purpose of the Study:
- To provide a comprehensive overview of methods for random-effects meta-analysis.
- To compare different approaches for calculating overall effect size estimates, confidence intervals, and prediction intervals.
- To identify preferable methods based on empirical evidence.
Main Methods:
- Review of existing literature on random-effects meta-analysis methods.
- Analysis of comparative simulation studies.
- Evaluation of real-life data studies.
Main Results:
- The DerSimonian and Laird method's statistical properties are suboptimal.
- Alternative methods offer improved performance in specific scenarios.
- Comparative studies guide the selection of appropriate methods.
Conclusions:
- Selecting the correct random-effects meta-analysis method is critical for accurate effect size estimation.
- Alternative methods to DerSimonian and Laird may provide more reliable results.
- Evidence from simulations and real data supports the use of certain alternative methods.
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