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Updated: May 8, 2026

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

A refined method for multivariate meta-analysis and meta-regression.

Daniel Jackson1, Richard D Riley

  • 1MRC Biostatistics Unit, Institute of Public Health, Cambridge, U.K.

Statistics in Medicine
|September 3, 2013
PubMed
Summary
This summary is machine-generated.

Accurate meta-analysis with few studies is challenging. A refined method using a scaling factor improves inference for average treatment effects in both univariate and multivariate meta-analysis, offering more precise results.

Keywords:
multivariate meta-analysismultivariate t distributionrandom effects modelssmall sample inference

Related Experiment Videos

Last Updated: May 8, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
08:36

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
  • Statistical Inference

Background:

  • Random effects models in meta-analysis are standard for estimating average treatment effects.
  • Inferences are unreliable with a small number of studies due to imprecise between-study variance estimates.
  • Conventional methods for confidence intervals and hypothesis testing are compromised in small meta-analyses.

Purpose of the Study:

  • To address the limitations of conventional random effects models in meta-analysis with few studies.
  • To introduce and validate a refined method for univariate meta-analysis.
  • To extend this refined method to multivariate meta-analysis and meta-regression for improved accuracy.

Main Methods:

  • A refined univariate meta-analysis method applying a scaling factor to the standard error of estimated effects.
  • Extension of the refined method to the multivariate meta-analysis and meta-regression settings.
  • Application of the proposed methodology to two real-world datasets.

Main Results:

  • The refined univariate method provides more accurate inference compared to conventional approaches.
  • The proposed multivariate meta-analysis and meta-regression methods yield more accurate inferences.
  • The methodology is implementable using standard outputs from existing meta-analysis software.

Conclusions:

  • A refined meta-analysis approach offers superior accuracy for average treatment effect estimation, especially with few studies.
  • The proposed scaling factor method enhances statistical inference in both univariate and multivariate meta-analysis.
  • This refined methodology provides a more reliable tool for researchers conducting meta-analyses.