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Estimation in meta-analyses of mean difference and standardized mean difference.

Ilyas Bakbergenuly1, David C Hoaglin2, Elena Kulinskaya1

  • 1School of Computing Sciences, University of East Anglia, Norwich, UK.

Statistics in Medicine
|November 12, 2019
PubMed
Summary

Estimating between-study variance (τ²) is crucial for random-effects meta-analysis. New methods for mean difference and standardized mean difference improve τ² estimation, enhancing overall effect estimates in meta-analyses.

Keywords:
between-study variancemean differencemeta-analysisrandom-effects modelstandardized mean difference

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Area of Science:

  • Statistics
  • Biostatistics
  • Medical Research Methodology

Background:

  • Random-effects meta-analysis relies on accurate estimation of between-study variance (τ²).
  • The performance of τ² estimators, in terms of bias and coverage, impacts the assessment of heterogeneity and overall effect estimation.
  • Existing τ² estimation methods exhibit variable performance across different effect measures.

Purpose of the Study:

  • To develop and evaluate novel methods for estimating τ² specifically for mean difference (MD) and standardized mean difference (SMD) effect measures.
  • To compare the performance of these new methods against existing τ² estimators through extensive simulations.
  • To provide evidence-based recommendations for τ² estimation based on effect measure.

Main Methods:

  • Development of new point and interval estimators for τ² using improved, effect-measure-specific approximations for the expected value of Q.
  • Introduction of Welch-type and corrected DerSimonian-Laird point estimators and a WT interval method for MD.
  • Introduction of a new point estimator and interval estimator for τ² in SMD.
  • Extensive simulations comparing proposed methods with established estimators (DerSimonian-Laird, REML, Mandel-Paule, Jackson) and interval methods (profile likelihood, Q-profile, Biggerstaff-Jackson, Jackson).
  • Evaluation of related overall effect estimators, including one using only sample size weights.

Main Results:

  • The performance of τ² estimators varies significantly depending on the effect measure used in meta-analysis.
  • New proposed methods demonstrate improved performance for MD and SMD compared to some existing estimators.
  • Simulation results provide a basis for measure-specific recommendations regarding τ² estimation.

Conclusions:

  • The choice of τ² estimation method is critical and should be tailored to the specific effect measure (MD or SMD).
  • The newly developed methods offer potentially more accurate and reliable estimates of between-study variance for MD and SMD.
  • These advancements can lead to more robust heterogeneity assessments and improved overall effect estimates in meta-analyses.