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Multivariate meta-analysis with an increasing number of parameters
Simina M Boca1,2,3, Ruth M Pfeiffer4, Joshua N Sampson4
1Innovation Center for Biomedical Informatics, Georgetown University Medical Center, 2115 Wisconsin Avenue, Suite 110, Washington, DC 20007, USA.
Biometrical Journal. Biometrische Zeitschrift
|February 15, 2017
Summary
Multivariate meta-analysis (MVMA) offers greater benefits than univariate meta-analysis (UVMA) as the number of parameters increases, especially in fixed-effect models. However, random-effects models show modest gains with more parameters due to complexity.
Area of Science:
- Biostatistics
- Epidemiology
- Statistical Modeling
Background:
- Meta-analysis synthesizes evidence from multiple studies.
- Univariate meta-analysis (UVMA) analyzes parameters individually.
- Multivariate meta-analysis (MVMA) analyzes parameters jointly, accounting for correlations.
Purpose of the Study:
- To compare the performance of MVMA and UVMA as the number of parameters (p) increases.
- To evaluate the impact of parameter count on meta-analysis efficiency.
Main Methods:
- Theoretical derivations.
- Simulation studies.
- Application to a meta-analysis of non-Hodgkin lymphoma risk factors.
Main Results:
- Fixed-effect (FE) MVMA shows increasing benefits with higher 'p'.
- Random-effects (RE) MVMA benefits increase with 'p' but are modest with high between-study variability.
- Efficiency loss in RE MVMA over FE MVMA increases with 'p' when between-study variability is low.
Conclusions:
- MVMA's advantages over UVMA grow with more parameters, particularly in FE models.
- RE MVMA performance is constrained by between-study variability and covariance matrix estimation.
- The choice between FE and RE MVMA is influenced by parameter count and study variability.
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