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Related Experiment Video

Updated: May 18, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Calculating sample sizes for cluster randomized trials: we can keep it simple and efficient!

Gerard J P van Breukelen¹, Math J J M Candel

¹Department of Methodology and Statistics, CAPHRI School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands. gerard.vbreukelen@maastrichtuniversity.nl

Journal of Clinical Epidemiology

|September 29, 2012

Summary

This summary is machine-generated.

Calculating efficient sample sizes for cluster randomized trials is simplified with new guidelines. These methods address unknown intraclass correlation (ICC) and varying cluster sizes, optimizing power and cost.

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Last Updated: May 18, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Area of Science:

Biostatistics
Epidemiology
Clinical Trials

Background:

Cluster randomized trials (CRTs) are essential for evaluating interventions in group-level settings.
Accurate sample size calculation is critical for the statistical power and efficiency of CRTs.
Challenges include unknown intraclass correlation (ICC) and variable cluster sizes.

Purpose of the Study:

To provide simple guidelines for calculating efficient sample sizes in CRTs.
To address the complexities of unknown ICC and varying cluster sizes.
To optimize designs for maximizing power or minimizing cost.

Main Methods:

Developed a simple equation for optimal number of clusters and sample size per cluster.
Addressed cluster size variation and ICC specification efficiently.
Defined optimality as maximizing power for a given budget or minimizing cost for a given power.

Main Results:

Optimal cluster numbers increase with ICC and decrease with cluster-to-person cost ratio.
Budget, power, and effect size influence cluster numbers, not per-cluster sample size.
Compensated for cluster size variation by sampling 10% more clusters.
A two-stage design is suggested, with the second stage used if ICC is higher than initially assumed.

Conclusions:

Efficient sample size calculations for CRTs are straightforward.
Accurate specification of per-cluster and per-person costs is necessary for computation.