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Basics of Multivariate Analysis in Neuroimaging Data
06:35

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Published on: July 24, 2010

Multivariate meta-analysis: potential and promise.

Dan Jackson1, Richard Riley, Ian R White

  • 1MRC Biostatistics Unit, Cambridge, U.K.. daniel.jackson@mrc-bsu.cam.ac.uk.

Statistics in Medicine
|January 27, 2011
PubMed
Summary
This summary is machine-generated.

Multivariate meta-analysis offers improved statistical properties over univariate methods but requires more assumptions. Careful application is crucial for realizing its potential in medical statistics.

Keywords:
multivariate meta-analysisrandom effects modelsstatistical software

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06:35

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Cross-Modal Multivariate Pattern Analysis
13:51

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Published on: November 9, 2011

Area of Science:

  • Statistics
  • Medical Statistics
  • Biostatistics

Background:

  • The multivariate random effects model generalizes standard univariate models.
  • Multivariate meta-analysis techniques and software are increasingly available.
  • A Royal Statistical Society event highlighted the growing use and considerations of multivariate methods.

Purpose of the Study:

  • To raise awareness of multivariate meta-analysis methods.
  • To discuss the advantages and disadvantages of these advanced statistical techniques.
  • To present a balanced account of discourse on multivariate meta-analysis.

Main Methods:

  • The article reviews applications, methods, difficulties, and arguments for/against multivariate meta-analysis.
  • Four contrasting examples are used to illustrate the concepts.
  • Discourse from a dedicated event informs the assessment.

Main Results:

  • Multivariate methods can yield estimates with superior statistical properties.
  • These benefits are balanced by increased assumption requirements.
  • Improved inference is not guaranteed in all cases.

Conclusions:

  • Multivariate meta-analysis shows considerable potential for statistical applications.
  • These methods necessitate careful application, exceeding the caution needed for univariate approaches.
  • Ensuring proper consideration by the medical statistics community is vital before widespread adoption.