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Related Experiment Video

Updated: Jul 18, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Bayesian design and analysis of two-arm cluster randomized trials using assurance.

Kevin J Wilson¹

¹School of Mathematics, Statistics & Physics, Newcastle University, Newcastle upon Tyne, UK.

Statistics in Medicine

|August 20, 2023

Summary

This study introduces a Bayesian approach for designing cluster randomized controlled trials (cRCTs). This method enhances sample size robustness and can reduce trial size when prior information suggests a larger treatment effect.

Keywords:

Bayesian design of experiments cluster RCT design and analysis priors sample size

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Area of Science:

Biostatistics
Clinical Trial Design

Background:

Cluster randomized controlled trials (cRCTs) are essential for evaluating interventions in group-level settings.
Accurate sample size determination is critical for the statistical power and efficiency of cRCTs.
Traditional methods for sample size calculation may be sensitive to parameter mis-specification.

Purpose of the Study:

To propose and evaluate a Bayesian approach for sample size determination in two-arm superiority cluster randomized controlled trials (cRCTs) with continuous outcomes.
To compare the Bayesian sample size approach with traditional methods, such as assurance based on a Wald test.
To provide guidance on selecting prior distributions and Monte Carlo sampling schemes for accurate sample size calculations.

Main Methods:

Utilizing Bayesian inference with a linear mixed-effects model for trial analysis.
Developing and evaluating an assurance metric for sample size selection via a two-loop Monte Carlo simulation scheme.
Assessing the impact of various prior distributions and Monte Carlo sample sizes on assurance and sample size determination.

Main Results:

The Bayesian approach offers increased robustness of the chosen sample size to parameter mis-specification.
Potential for reduced sample sizes when prior information suggests a clinically important treatment effect.
Demonstrated application to sample size calculation for a cRCT in poststroke incontinence.

Conclusions:

The proposed Bayesian design and analysis framework provides a robust and potentially more efficient method for sample size determination in cRCTs.
This approach can lead to more reliable sample size estimates, especially when incorporating prior knowledge.
The Bayesian method is particularly advantageous when prior information indicates a potentially larger treatment effect than the minimal clinically important difference.