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Assessing meta-regression methods for examining moderator relationships with dependent effect sizes: A Monte Carlo

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Robust variance estimation and 3-level modeling effectively handle dependent effect sizes in meta-regression. Standard methods inflate Type I errors, while robust variance estimation offers better error control but lower power, and 3-level models show promise for larger datasets.

Keywords:
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Area of Science:

  • Biostatistics
  • Meta-analysis
  • Quantitative Synthesis

Background:

  • Dependent effect sizes are common in meta-analysis, complicating standard analytical approaches.
  • Accurate statistical inference requires methods that properly account for effect size dependency.

Purpose of the Study:

  • To compare the performance of robust variance estimation (RVE) and 3-level modeling against standard methods for meta-regression with dependent effect sizes.
  • To evaluate small-sample adjustments for RVE and hypothesis testing methods for 3-level models.

Main Methods:

  • Monte Carlo simulation was used to compare statistical methods.
  • Evaluated bias in slope estimates, confidence interval width, Type I error rates, and statistical power.
  • Compared methods under varying conditions, including moderator placement and study characteristics.

Main Results:

  • Standard methods ignoring dependency inflated Type I error rates for moderator significance testing.
  • Robust variance estimation (RVE) provided the best Type I error control but resulted in wider confidence intervals and lower statistical power, especially with jackknife adjustments.
  • Three-level models demonstrated strong performance, particularly with the likelihood ratio test, offering narrower confidence intervals and higher power than RVE in moderate to large studies.

Conclusions:

  • Robust variance estimation and 3-level modeling are superior to standard methods when dealing with dependent effect sizes in meta-regression.
  • Three-level models are a promising alternative, especially for meta-analyses with a moderate to large number of studies.
  • Method performance is enhanced with moderators at the effect size level, larger study counts, and smaller between-studies variance.