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Updated: May 11, 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

Avoiding zero between-study variance estimates in random-effects meta-analysis.

Yeojin Chung1, Sophia Rabe-Hesketh, In-Hee Choi

  • 1School of Business Administration, Kookmin University, Seoul, Korea. ychung@kookmin.ac.kr

Statistics in Medicine
|May 15, 2013
PubMed
Summary
This summary is machine-generated.

Bayes modal (BM) estimation offers a solution to boundary estimates in random-effects meta-analysis. This method improves parameter uncertainty and provides better coverage for overall effects in heterogeneous data.

Keywords:
Bayesian posterior modepenalized maximum likelihoodrandom-effects meta-analysisvariance estimation

Related Experiment Videos

Last Updated: May 11, 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:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Fixed-effects meta-analysis often assumes unrealistic homogeneity, leading to underestimated parameter uncertainty.
  • Random-effects meta-analysis and meta-regression are used to account for variability, but can yield boundary estimates for between-study standard deviation.
  • Boundary estimates of zero for between-study standard deviation result in fixed-effects estimates, compromising accuracy.

Purpose of the Study:

  • To propose Bayes modal (BM) estimation using a gamma prior to avoid boundary estimates in meta-analysis and meta-regression.
  • To compare the performance of BM estimation against commonly used classical and Bayesian methods.
  • To evaluate the effectiveness of BM estimation in scenarios with unexplained heterogeneity.

Main Methods:

  • A novel Bayes modal (BM) estimation approach with a gamma prior on the between-study standard deviation was developed.
  • Existing meta-analysis and meta-regression estimation methods, including classical and Bayesian approaches, were reviewed and applied.
  • The proposed BM estimator and other methods were applied to real datasets and compared using simulations.

Main Results:

  • BM estimation successfully avoided boundary estimates for the between-study standard deviation.
  • The root mean squared error for the between-study standard deviation was smaller with BM estimation compared to other methods.
  • BM estimation demonstrated superior coverage for overall effects in the presence of small to moderate unexplained heterogeneity.

Conclusions:

  • Bayes modal estimation provides a robust alternative for handling between-study variability in meta-analysis and meta-regression.
  • The method is particularly advantageous when dealing with unexplained heterogeneity, offering more reliable parameter estimates and improved uncertainty quantification.
  • BM estimation with a weakly informative default prior is recommended when prior information on heterogeneity is unavailable.