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Multiple moderator meta-analysis using the R-package Meta-CART.

Xinru Li1, Elise Dusseldorp2, Xiaogang Su3

  • 1Mathematical Institute, Leiden University, P.O. Box 9512, 2300, RA, Leiden, The Netherlands.

Behavior Research Methods
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Summary
This summary is machine-generated.

This study introduces meta-CART, a new method for meta-analysis that identifies how multiple study features (moderators) interact to explain differences in results. The R-package metacart makes this advanced technique accessible for researchers.

Keywords:
CARTComputer softwareFixed effectHeterogeneityInteraction between moderatorsMeta-analysisRandom effects

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Psychometrics

Background:

  • Meta-analysis often reveals significant heterogeneity between study results.
  • Understanding moderators that explain this heterogeneity is crucial for intervention effectiveness.
  • Interaction effects between multiple moderators are frequently overlooked in traditional meta-analysis.

Purpose of the Study:

  • To introduce the meta-CART method for identifying interactions among multiple moderators in meta-analysis.
  • To present the R-package `metacart` for user-friendly implementation of meta-CART.
  • To provide a practical tool for partitioning studies into more homogeneous subgroups based on moderator combinations.

Main Methods:

  • The paper describes the meta-CART algorithm, a tree-based approach to model moderator interactions.
  • The `metacart` R-package is detailed, offering functions for fixed- and random-effects meta-CART.
  • The package accommodates various moderator types: dichotomous, categorical, ordinal, and continuous.

Main Results:

  • The `metacart` package enables the identification of complex interaction patterns among moderators.
  • The tree model partitions studies into subgroups with reduced heterogeneity.
  • A novel look-ahead procedure is incorporated for enhanced analysis.

Conclusions:

  • The `metacart` R-package democratizes the use of advanced meta-analytic techniques for exploring moderator interactions.
  • Researchers can better assess intervention effectiveness and design future studies by understanding these interactions.
  • The package facilitates a more nuanced understanding of heterogeneity in meta-analysis.