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A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and
Dan Jackson1, Ian R White, Richard D Riley
1MRC Biostatistics Unit, Cambridge CB2 0SR, UK. daniel.jackson@mrc-bsu.cam.ac.uk
Biometrical Journal. Biometrische Zeitschrift
|February 13, 2013
Summary
A new multivariate method of moments estimates the between-study covariance matrix, accommodating complete or incomplete outcomes and covariates in meta-regression. This approach extends the standard univariate method of moments for robust multivariate meta-analysis.
Area of Science:
- Biostatistics
- Epidemiology
- Medical Research
Background:
- Multivariate meta-analysis is increasingly utilized for synthesizing complex health data.
- Existing methods for fitting multivariate random effects models include maximum likelihood, restricted maximum likelihood, and Bayesian estimation.
- Current methods often have limitations regarding data completeness and covariate inclusion.
Purpose of the Study:
- To introduce a novel multivariate method of moments for estimating the between-study covariance matrix.
- To develop a method that can handle both complete and incomplete outcome data.
- To incorporate covariates through meta-regression within the multivariate meta-analysis framework.
Main Methods:
- A new multivariate method of moments is proposed.
- The method is designed to be invariant to linear transformations for complete data.
- It generalizes the DerSimonian and Laird univariate method of moments to multiple dimensions.
Main Results:
- The proposed method effectively estimates the between-study covariance matrix.
- It accommodates missing outcome data and allows for meta-regression.
- The method demonstrated robustness in simulation studies and a real-world example.
Conclusions:
- The novel multivariate method of moments offers a flexible and robust approach for complex meta-analyses.
- This method enhances the ability to synthesize evidence when dealing with incomplete data and covariates.
- It provides a valuable alternative to existing estimation techniques in multivariate meta-analysis.
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