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Location-scale models for meta-analysis.

Wolfgang Viechtbauer1, José Antonio López-López2

  • 1Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, The Netherlands.

Research Synthesis Methods
|April 19, 2022
PubMed
Summary
This summary is machine-generated.

Researchers can now explore how predictors influence heterogeneity in meta-analysis. A new location-scale model extends standard methods, allowing analysis of both outcome size and heterogeneity variance.

Keywords:
heterogeneitylocation-scale modelmeta-regressionmixed-effects models

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Area of Science:

  • Statistical modeling
  • Evidence synthesis
  • Meta-analysis methodology

Background:

  • Heterogeneity is common in meta-analysis, often analyzed using mixed-effects meta-regression.
  • Standard models assume constant heterogeneity variance, limiting exploration of its predictors.
  • Existing methods cannot examine heterogeneity's amount as a function of predictors.

Approach:

  • Introduced a location-scale model for meta-analysis, extending random- and mixed-effects models.
  • This model examines predictors related to outcome size (location) and heterogeneity (scale).
  • Employs maximum and restricted maximum likelihood for estimation, with inference and visualization methods.

Key Points:

  • The location-scale model allows simultaneous investigation of predictors' effects on outcome location and scale.
  • Provides estimation and inference methods for analyzing heterogeneity variance.
  • An R package implementation (metafor) is available for practical application.

Conclusions:

  • Location-scale models offer a powerful extension to standard meta-analysis techniques.
  • Enables researchers to investigate heterogeneity more comprehensively.
  • Enhances the scope of research questions addressable in evidence synthesis.