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Bootstrap-Based Between-Study Heterogeneity Tests in Meta-Analysis.

Han Du1, Ge Jiang2, Zijun Ke3

  • 1Department of Psychology, University of California, Los Angeles.

Multivariate Behavioral Research
|January 24, 2022
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
Meta-analysisbetween-study heterogeneitybootstrap method

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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.