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Related Experiment Video

Updated: Jul 13, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

An alternative model for bivariate random-effects meta-analysis when the within-study correlations are unknown.

Richard D Riley1, John R Thompson, Keith R Abrams

  • 1Centre for Medical Statistics and Health Evaluation, Faculty of Medicine, University of Liverpool, Liverpool, England L69 3GS. richard.riley@liv.ac.uk

Biostatistics (Oxford, England)
|July 13, 2007
PubMed
Summary

This study introduces a new bivariate random-effects meta-analysis (BRMA) model that simplifies synthesizing correlated endpoints by removing the need for within-study correlations. The proposed model offers accurate pooled estimates and improved statistical properties compared to separate univariate analyses, enhancing meta-analysis applications.

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Last Updated: Jul 13, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Biostatistics
  • Epidemiology
  • Medical Research Synthesis

Background:

  • Multivariate meta-analysis synthesizes multiple correlated endpoints, but requires known within-study correlations, limiting practical application.
  • Hierarchical multivariate random-effects meta-analysis models incorporate both within- and between-study correlations.

Purpose of the Study:

  • To propose an alternative bivariate random-effects meta-analysis (BRMA) model that does not require known within-study correlations.
  • To enable practical application of multivariate meta-analysis for correlated endpoints in evidence synthesis.

Main Methods:

  • Developed an alternative bivariate random-effects meta-analysis (BRMA) model using only one overall correlation parameter (rho).
  • The model requires data equivalent to separate univariate random-effects meta-analyses (URMA) for each endpoint.
  • Performance evaluated through analytic assessment, simulation studies, and literature data application.

Main Results:

  • The alternative BRMA model produces pooled estimates similar to hierarchical models, with little bias, even without known within-study correlations.
  • It demonstrates superior statistical properties compared to separate URMAs, particularly with missing data.
  • The model is less prone to estimation boundary issues than fully hierarchical models.

Conclusions:

  • The alternative BRMA model effectively synthesizes correlated endpoints without requiring within-study correlations, making it broadly applicable.
  • This approach facilitates the use of correlation in meta-analysis, potentially increasing the adoption of BRMA.
  • It offers a practical solution for evidence synthesis involving multiple correlated outcomes.