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Published on: November 27, 2019
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Permutation inference methods for multivariate meta-analysis
Hisashi Noma1, Kengo Nagashima2, Toshi A Furukawa3
1Department of Data Science, The Institute of Statistical Mathematics, Tokyo, Japan.
Biometrics
|August 11, 2019
Summary
New permutation-based methods offer exact inferences for multivariate meta-analyses, overcoming limitations of standard random-effects models, especially with small study numbers. These approaches ensure accurate confidence intervals and regions for synthesized outcomes.
Area of Science:
- Biostatistics
- Evidence Synthesis
- Meta-Analysis
Background:
- Multivariate meta-analysis synthesizes multiple correlated outcomes, commonly using random-effects models.
- Standard inference methods for random-effects models lack accurate coverage probabilities, particularly with few studies, due to reliance on large sample approximations.
- Existing methods exhibit undercoverage issues, compromising the reliability of confidence intervals and regions.
Purpose of the Study:
- To develop novel permutation-based inference methods for exact joint and marginal inferences in multivariate meta-analyses.
- To provide accurate statistical inference that does not depend on large sample approximations.
- To address the undercoverage problems associated with current standard inference techniques.
Main Methods:
- Development of permutation-based inference methods for exact joint inferences of average outcome measures.
- Introduction of accurate marginal inference methods applicable under general multivariate meta-analysis settings.
- Utilization of optimal weighting based on the efficient score statistic for permutation inferences.
Main Results:
- Proposed permutation methods enable exact joint inferences without large sample approximations.
- Accurate marginal inference methods were developed for general multivariate meta-analysis scenarios.
- Simulations and applications demonstrated that the new methods provide accurate confidence regions/intervals, unlike standard methods which showed significant undercoverage.
Conclusions:
- The novel permutation-based inference methods offer a robust solution for multivariate meta-analysis, ensuring reliable statistical inference.
- These methods effectively address the limitations of traditional approaches, particularly in scenarios with a small number of studies.
- The proposed techniques provide accurate and dependable confidence intervals and regions, enhancing the quality of evidence synthesis.
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