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The application of meta-analytic (multi-level) models with multiple random effects: A systematic review.

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Multilevel models with multiple random effects offer advanced ways to analyze meta-analysis data. However, complex models like four- or five-level and cross-classified random effects models are underutilized, impacting realistic simulation studies.

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Area of Science:

  • Statistics
  • Biostatistics
  • Meta-analysis

Background:

  • Meta-analysis involves multilevel data structures where participants are nested within studies.
  • Traditional random effects models account for study effects, but additional random effects can model dependencies within or across studies.

Purpose of the Study:

  • To describe the application of multilevel models with multiple random effects in meta-analysis.
  • To illustrate how more sophisticated models could address data dependencies in three-level meta-analyses.
  • To characterize multilevel meta-analyses for improving future simulation studies.

Main Methods:

  • Systematic review of multilevel meta-analysis applications.
  • Analysis of data structures and dependencies in meta-analytic datasets.
  • Evaluation of simulation study conditions in multilevel meta-analysis.

Main Results:

  • Hierarchical (three-, four-, five-level) and cross-classified random effects models are infrequently used in meta-analysis.
  • Underutilization of complex models may not fully capture meta-analytic data structures.
  • Simulation studies on multilevel meta-analysis often lack realistic factor conditions.

Conclusions:

  • There is a need for greater adoption of advanced multilevel models in meta-analysis.
  • Future simulation studies should incorporate more realistic conditions based on current meta-analytic practices.
  • Improved modeling can enhance the accuracy and applicability of meta-analysis findings.