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Performance of location-scale models in meta-analysis: A simulation study.

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Location-scale models in meta-analysis help study effect variance. Restricted maximum likelihood estimation and permutation tests offer improved statistical properties for analyzing heterogeneity.

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Area of Science:

  • Meta-analysis
  • Statistical modeling
  • Heterogeneity analysis

Background:

  • Location-scale models in meta-analysis enable simultaneous examination of moderator effects on both the mean (location) and variance (scale) of true effect distributions.
  • The complexity of these models presents challenges in fitting and lacks systematic examination of estimation and inference methods in meta-analysis.

Purpose of the Study:

  • To compare different estimation methods, significance tests, and confidence interval construction methods for location-scale models in meta-analysis.
  • To evaluate the statistical properties of these methods in the context of meta-analytic heterogeneity.

Main Methods:

  • A Monte Carlo simulation study was conducted.
  • Compared maximum likelihood and restricted maximum likelihood estimation.
  • Evaluated Wald-type, permutation, and likelihood-ratio tests for significance.
  • Assessed Wald-type and profile-likelihood confidence intervals for scale coefficients.

Main Results:

  • Restricted maximum likelihood estimation yielded rejection rates closer to nominal levels and narrower confidence intervals.
  • Permutation tests showed type I error rates closest to the nominal level; likelihood-ratio tests had the highest statistical power.
  • Profile-likelihood intervals had lower coverage probabilities than Wald-type but were closer to the nominal 95% level.
  • Dichotomous moderators resulted in slightly higher rejection rates and coverage probabilities than continuous moderators.

Conclusions:

  • Location-scale models are valuable tools for modeling heterogeneity in meta-analysis.
  • Despite potential challenges like parameter space constraints and non-convergence, these models offer a robust approach.
  • Restricted maximum likelihood estimation and permutation tests show promising statistical properties for scale coefficient analysis.