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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Published on: July 3, 2020

A method of moments estimator for random effect multivariate meta-analysis.

Han Chen1, Alisa K Manning, Josée Dupuis

  • 1Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA. hanchen@bu.edu

Biometrics
|May 4, 2012
PubMed
Summary

This study introduces a new, noniterative method for random effect model multivariate meta-analysis. The novel approach accurately estimates between-study covariance, improving upon existing methods in simulations and real-world data analysis.

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Epidemiological Research

Background:

  • Meta-analysis combines evidence from multiple studies, crucial for parameter inference.
  • Fixed effect models assume identical effect sizes, failing with heterogeneity and risking false positives.
  • Random effect models account for within- and between-study variance, offering a more conservative approach.

Purpose of the Study:

  • To develop a noniterative method of moments estimator for the between-study covariance matrix in random effect model multivariate meta-analysis.
  • To provide the first matrix-form method of moments estimator for this purpose.
  • To offer a statistically robust alternative for handling heterogeneity in multivariate meta-analyses.

Main Methods:

  • Development of a novel noniterative method of moments estimator.
  • Extension of the DerSimonian and Laird univariate estimator to a multivariate matrix form.
  • Validation through simulation studies and application to a real-world dataset.

Main Results:

  • The proposed estimator is a multivariate extension of the DerSimonian and Laird estimator.
  • The estimator is invariant to linear transformations, enhancing its applicability.
  • Simulation results demonstrate competitive performance against existing random effect model multivariate meta-analysis methods.

Conclusions:

  • The novel noniterative method provides an effective and robust approach for random effect model multivariate meta-analysis.
  • This method addresses the challenge of estimating between-study covariance in the presence of heterogeneity.
  • The approach is validated through simulations and a practical data example, suggesting broad utility.