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Basics of Multivariate Analysis in Neuroimaging Data
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Published on: July 24, 2010

A practical introduction to multivariate meta-analysis.

Dimitris Mavridis1, Georgia Salanti

  • 1Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.

Statistical Methods in Medical Research
|January 26, 2012
PubMed
Summary
This summary is machine-generated.

This review covers statistical methods and software for multivariate meta-analysis. It highlights Bayesian approaches using Markov chain Monte Carlo, offering practical guidance and code examples for implementation.

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Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Multivariate meta-analysis is gaining traction in research synthesis.
  • Statistical software increasingly incorporates routines for this complex analysis.

Purpose of the Study:

  • To review statistical methods for multivariate meta-analysis.
  • To survey available software and provide practical implementation guidance.

Main Methods:

  • Focus on Bayesian inference using Markov chain Monte Carlo (MCMC).
  • Illustrate model-fitting options with practical examples.
  • Provide specific guidance for using various software packages.

Main Results:

  • Comprehensive overview of current multivariate meta-analysis techniques.
  • Demonstration of Bayesian MCMC methods with WinBUGS code.
  • Practical examples showcasing model-fitting and software application.

Conclusions:

  • Bayesian methods offer a powerful framework for multivariate meta-analysis.
  • Accessible guidance and code facilitate the application of these methods.
  • The review aims to enhance the adoption and correct application of multivariate meta-analysis.