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Updated: Jun 4, 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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Comparing methods to estimate treatment effects on a continuous outcome in multicentre randomized controlled trials:

Rong Chu1, Lehana Thabane, Jinhui Ma

  • 1Department of Clinical Epidemiology and Biostatistics, McMaster University, Health Sciences Centre, Room 2C7, 1200 Main Street West, Hamilton ON, L8N 3Z5, Canada. chur@mcmaster.ca

BMC Medical Research Methodology
|February 23, 2011
PubMed
Summary

For multicentre randomized controlled trials, adjusting for centre as a random effect is the most efficient statistical method for estimating treatment effects. This approach ensures optimal precision and power across various scenarios.

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Last Updated: Jun 4, 2026

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Published on: January 8, 2020

Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Statistical Modeling

Background:

  • Multicentre randomized controlled trials (RCTs) commonly employ stratification by centre.
  • A consensus on analyzing correlated continuous outcomes in these trials is lacking.
  • Investigating statistical models for clustered data in multicentre RCTs is crucial.

Purpose of the Study:

  • To compare the performance of six statistical models for analyzing continuous outcomes in multicentre RCTs.
  • To evaluate models under varying intraclass correlation (ICC) coefficients, number of centres, and centre sizes.
  • To identify the most efficient and reliable method for treatment effect estimation.

Main Methods:

  • Simulations were conducted comparing six methods: ignoring centres, fixed effects, random effects, generalized estimating equation (GEE), and centre-level fixed/random effects.
  • Models were assessed for bias, precision, mean squared error, confidence interval coverage, and statistical power.
  • Analyses assumed no treatment by centre interaction.

Main Results:

  • All methods provided unbiased treatment effect estimates.
  • Ignoring centres reduced power when intraclass correlation was present.
  • Mixed-effects models demonstrated superior efficiency, achieving nominal coverage and power across most scenarios.
  • GEE underestimated standard errors with few centres; centre-level models showed increased variability or lower coverage/power.

Conclusions:

  • All six models yield unbiased treatment effect estimates in multicentre trials.
  • Adjusting for centre as a random intercept is the most efficient method for treatment effect estimation.
  • This holds true across simulations assuming normality and no treatment by centre interaction.