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Related Experiment Video

Updated: May 20, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Quantifying the impact of between-study heterogeneity in multivariate meta-analyses.

Dan Jackson¹, Ian R White, Richard D Riley

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

Statistics in Medicine

|July 6, 2012

Summary

New statistics quantify heterogeneity in multivariate meta-analysis, extending the popular I(2) statistic. These methods, including I(R)(2) and I(H)(2), are applicable to random effects models and meta-regression.

Related Experiment Videos

Last Updated: May 20, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Area of Science:

Biostatistics
Medical Research Methodology

Background:

Univariate meta-analysis commonly uses heterogeneity measures like the I(2) statistic.
Multivariate meta-analysis, pooling multiple outcomes, is increasingly prevalent.
Quantifying heterogeneity in multivariate settings remains a challenge.

Purpose of the Study:

To develop novel statistics for quantifying heterogeneity in multivariate meta-analysis.
To provide a generalized framework for assessing heterogeneity in complex meta-analytic models.

Main Methods:

Generalizing the univariate R(2) statistic to create a multivariate analogue, termed I(R)(2).
Developing a multivariate H(2) statistic and its associated I(H)(2) measure.
Applying these statistics within multivariate random effects models and meta-regression.

Main Results:

Proposed statistics I(R)(2) and I(H)(2) effectively quantify heterogeneity in multivariate meta-analysis.
The methods are compatible with standard multivariate meta-analysis procedures and estimates.
Demonstrated applicability to real datasets and multivariate meta-regression.

Conclusions:

The new heterogeneity statistics offer robust tools for multivariate meta-analysis and meta-regression.
These measures enhance the interpretation of results from complex meta-analytic models.
The proposed statistics are suitable for any procedure fitting multivariate random effects models.