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Heterogeneity estimates in a biased world.

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  • 1Department of Psychology, Northumbria University, Newcastle upon Tyne, United Kingdom.

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Summary
This summary is machine-generated.

This study simulated meta-analyses to evaluate heterogeneity estimators. Biases in primary studies significantly impacted effect size estimation more than heterogeneity estimation, with REML performing well overall.

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Area of Science:

  • Psychology
  • Statistics
  • Biostatistics

Background:

  • Meta-analyses quantify effect consistency across studies.
  • Numerous heterogeneity estimators exist, but their performance under biased study conditions is less understood.
  • Previous evaluations often assumed unbiased primary studies, which is unrealistic.

Purpose of the Study:

  • To evaluate five heterogeneity estimators in meta-analyses of continuous outcomes.
  • To assess the impact of biases in primary studies on effect size and heterogeneity estimation.
  • To identify optimal estimators under various simulated research conditions.

Main Methods:

  • Computer simulations manipulated six factors: publication bias strength and type, p-hacking prevalence, true heterogeneity, true effect size, and number of studies.
  • Evaluated five heterogeneity estimators: REML, Paule-Mandel, DerSimonian-Laird, and others.
  • Focused on meta-analyses of continuous outcome measures.

Main Results:

  • Biases in primary studies posed greater challenges for effect size estimation than for heterogeneity estimation.
  • Estimation bias for heterogeneity remained small to moderate in most simulated conditions.
  • The REML and DerSimonian-Laird estimators performed well under biased primary study conditions for heterogeneity estimation.

Conclusions:

  • The REML estimator is a robust choice for meta-analyses of continuous outcomes, even with potential biases in primary studies.
  • While Paule-Mandel performed well with unbiased studies, it faltered under bias.
  • Effect size estimation was less sensitive to the choice of heterogeneity estimator than heterogeneity estimation itself.