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metapack: An R Package for Bayesian Meta-Analysis and Network Meta-Analysis with a Unified Formula Interface
Daeyoung Lim1, Ming-Hui Chen1, Joseph G Ibrahim2
1University of Connecticut, 215 Glenbrook Rd. U-4120 Storrs, CT 06269-4120, United States.
The R Journal
|May 11, 2023
Summary
This paper introduces metapack, an R package simplifying complex meta-analysis and network meta-analysis. It offers flexible modeling and visualization tools for researchers, enhancing data synthesis and statistical inference.
Area of Science:
- Biostatistics
- Statistical Modeling
- Data Synthesis
Background:
- Meta-analysis is a statistical method for combining research findings.
- Aggregate data meta-analyses offer flexibility and are widely used.
- Complex statistical models hinder the adoption of advanced meta-analysis techniques.
Purpose of the Study:
- To introduce the R package metapack.
- To provide a unified formula interface for meta-analysis and network meta-analysis.
- To facilitate flexible variance-covariance modeling for multivariate and univariate network meta-analysis.
Main Methods:
- Development of the R package metapack.
- Implementation of a unified formula interface for meta-analysis and network meta-analysis.
- Inclusion of functions for plotting and statistical inference, including model assessment.
Main Results:
- The metapack package offers a user-friendly interface for complex meta-analysis models.
- Flexible variance-covariance modeling is supported for multivariate meta-analysis and univariate network meta-analysis.
- Demonstration of use cases with two real datasets included in the package.
Conclusions:
- Metapack simplifies the application of advanced meta-analysis and network meta-analysis techniques.
- The package enhances statistical inference and model assessment capabilities.
- Metapack promotes wider adoption of sophisticated meta-analysis methods in research.
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