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Meta-analysis of multiple outcomes by regression with random effects

C S Berkey1, D C Hoaglin, A Antczak-Bouckoms

  • 1Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA. catherine.berkey@channing.harvard.edu

Statistics in Medicine
|December 5, 1998
PubMed
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New random-effects models improve regression meta-analysis for multiple correlated outcomes, addressing unexplained heterogeneity and potential bias found in fixed-effects models.

Area of Science:

  • Biostatistics
  • Clinical Trial Analysis
  • Epidemiology

Background:

  • Fixed-effects meta-analysis can jointly analyze multiple correlated outcomes but may be biased by unexplained heterogeneity.
  • Existing methods may not adequately account for residual variation among studies after covariate adjustment.

Purpose of the Study:

  • To propose and evaluate novel random-effects regression models for meta-analysis of multiple correlated outcomes.
  • To compare the performance of these new models against fixed-effects and separate-outcomes approaches.

Main Methods:

  • Developed two random-effects regression models for handling multiple, correlated outcomes in meta-analysis.
  • Compared proposed models with fixed-effects generalized-least-squares and separate-outcomes models.
  • Utilized a simulation study and a meta-analysis of periodontal clinical trials for evaluation.

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Main Results:

  • Random-effects models demonstrated advantages over fixed-effects and separate-outcomes models in simulation studies.
  • The proposed methods effectively handle unexplained heterogeneity in regression meta-analysis.
  • New approaches facilitate meta-analysis of trials with more than two treatment arms.

Conclusions:

  • Random-effects regression meta-analysis is a superior approach for multiple correlated outcomes when unexplained heterogeneity exists.
  • These methods offer a more robust and less biased alternative to fixed-effects models.
  • The proposed techniques enhance the analysis of complex clinical trial data and comparative effectiveness research.