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Bootstrap-Based Between-Study Heterogeneity Tests in Meta-Analysis
Multivariate Behavioral Research
|January 24, 2022
Summary
New bootstrap methods improve heterogeneity testing in meta-analysis, addressing limitations of existing Q and likelihood ratio (LR) tests. The B-REML-LRT method is recommended for its superior performance in controlling errors and increasing statistical power.
Area of Science:
- Statistical methodology
- Biostatistics
- Meta-analysis research
Background:
- Testing between-study heterogeneity is crucial but challenging in meta-analysis.
- Existing heterogeneity tests (Q, LR) suffer from Type I error rate issues and low statistical power.
- Current methods lack maturity for testing heterogeneity above specific thresholds.
Approach:
- Proposed novel heterogeneity tests by integrating bootstrap methods with existing tests (maximum LR, restricted maximum LR, Q tests).
- Developed B-ML-LRT, B-REML-LRT, and B-Q methods to address reference distribution limitations.
- Conducted simulation studies to compare proposed methods against standard LR, Q, and Kulinskaya's improved Q tests.
Key Points:
- B-REML-LRT demonstrated superior performance in controlling Type I error rates and achieving higher statistical power.
- The improved Q test is also recommended when applicable.
- An R package is available for implementing the proposed bootstrap-based heterogeneity tests.
Conclusions:
- Bootstrap methods offer a robust solution for heterogeneity testing in meta-analysis.
- B-REML-LRT is the recommended approach for reliable heterogeneity assessment.
- The developed methods and R package enhance the practical application of advanced meta-analysis techniques.
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