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A flexible approach to identify interaction effects between moderators in meta-analysis.

Xinru Li1, Elise Dusseldorp2, Jacqueline J Meulman1

  • 1Mathematical Institute, Leiden University, Leiden, The Netherlands.

Research Synthesis Methods
|December 5, 2018
PubMed
Summary

This study introduces an improved meta-CART method for analyzing moderator interactions in meta-analysis. The enhanced approach integrates interaction identification and testing, offering better power for complex moderator effects.

Keywords:
CARTfixed effectinteraction between moderatorsmeta-analysisrandom effects

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Area of Science:

  • Statistics
  • Biostatistics
  • Meta-analysis

Background:

  • Traditional meta-analysis methods struggle with detecting high-order moderator interactions.
  • Existing meta-CART approaches involve separate steps for interaction identification and significance testing.

Purpose of the Study:

  • To enhance meta-CART by integrating interaction identification and subgroup analysis into a single step.
  • To consistently incorporate fixed-effect or random-effects assumptions throughout the meta-CART process.

Main Methods:

  • Developed an integrated meta-CART approach, adapting methods for fixed-effect and a new algorithm for random-effects models.
  • Evaluated the performance using extensive simulations with various moderator variable types (dichotomous, nominal, ordinal, continuous).

Main Results:

  • The improved meta-CART demonstrated satisfactory performance with power > 0.80 and Type I error < 0.05.
  • Sufficient performance requires an appropriate pruning rule and a minimum number of studies (40-120), depending on effect complexity and heterogeneity.

Conclusions:

  • The enhanced meta-CART provides a powerful tool for investigating complex moderator interactions in meta-analysis.
  • Optimal application depends on study numbers, effect characteristics, and data heterogeneity, guiding researchers in its effective use.