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A matrix-based method of moments for fitting multivariate network meta-analysis models with multiple outcomes and
Dan Jackson1, Sylwia Bujkiewicz2, Martin Law1
1MRC Biostatistics Unit, Cambridge, U.K.
Biometrics
|August 15, 2017
Summary
This study introduces a new statistical model for multivariate network meta-analysis, enabling simultaneous analysis of multiple outcomes and treatments. The method addresses heterogeneity and inconsistency, enhancing evidence synthesis in medical research.
Area of Science:
- Biostatistics
- Medical Statistics
- Evidence Synthesis
Background:
- Random-effects meta-analyses are widely used in medical research.
- Recent advancements include multivariate and network meta-analysis for multiple outcomes and treatments, respectively.
Purpose of the Study:
- To present a novel statistical model and estimation procedure for multivariate network meta-analysis.
- To integrate multiple outcomes and treatments within a single analytical framework.
Main Methods:
- Developed a multivariate model extending univariate network meta-analysis.
- Incorporated variance parameters for between-study heterogeneity and inconsistency.
- Proposed an estimation procedure building upon existing meta-analysis methods like DerSimonian and Laird.
Main Results:
- The new model effectively handles multiple outcomes and treatments in meta-analysis.
- The estimation procedure is robust and extends established methods.
- Investigated performance through simulation and a real-world case study.
Conclusions:
- The proposed multivariate network meta-analysis model offers a unified approach for complex evidence synthesis.
- This methodology enhances the ability to analyze multiple outcomes and treatments concurrently.
- The approach provides a valuable tool for medical statisticians and researchers.
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