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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

Higher-order likelihood inference in meta-analysis and meta-regression.

Annamaria Guolo1

  • 1University of Verona, via dell'Artigliere 19, I-37129, Italy. annamaria.guolo@univr.it

Statistics in Medicine
|December 17, 2011
PubMed
Summary

Likelihood methods in random-effects meta-analysis can be misleading with small samples. Higher-order asymptotic adjustments significantly improve accuracy, offering a computationally feasible solution for more reliable meta-analysis results.

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Last Updated: May 26, 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
  • Statistical Inference
  • Meta-Analysis

Background:

  • Meta-analysis is crucial for synthesizing research findings.
  • Random-effects models are commonly used in meta-analysis.
  • First-order likelihood inference methods can yield inaccurate results, especially with small sample sizes typical in meta-analysis.

Purpose of the Study:

  • To investigate the performance of likelihood methods in random-effects meta-analysis.
  • To address the limitations of first-order approximations in meta-analysis.
  • To introduce and evaluate higher-order asymptotic adjustments for improved likelihood inference.

Main Methods:

  • Utilized the framework of random-effects models for meta-analysis.
  • Applied higher-order asymptotic theory, specifically a second-order adjustment to the log-likelihood ratio statistic.
  • Conducted simulation studies for meta-analysis and meta-regression to compare methods.

Main Results:

  • First-order likelihood inference can produce misleading results, particularly with small sample sizes.
  • Higher-order likelihood inference demonstrates significantly improved accuracy compared to first-order methods.
  • The proposed higher-order approach is computationally feasible.

Conclusions:

  • Higher-order asymptotic adjustments offer a more accurate and reliable approach to likelihood inference in random-effects meta-analysis.
  • The findings are particularly relevant for meta-analyses with small sample sizes.
  • The proposed method provides a practical enhancement to existing meta-analysis techniques.