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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

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Properties of adaptive, cluster-randomised controlled trials with few clusters: a simulation study.

Erin Nolan^1,2,3, Joshua Dizon^4,5, Christopher Oldmeadow^4,5

¹School of Medicine and Public Health, The University of Newcastle, Callaghan, NSW, Australia. Erin.Nolan@hmri.org.au.

Implementation Science : IS

|July 2, 2025

Summary

Adaptive designs are feasible for cluster randomized trials with few clusters, offering efficiency gains. However, high intra-class correlation can lead to incorrect decisions, reducing feasibility.

Keywords:

Adaptive designs Bayesian Implementation Optimisation Simulation

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Area of Science:

Health Services Research
Clinical Trials Methodology
Implementation Science

Background:

Optimizing implementation strategies involves complex, multi-component interventions assessed via cluster randomized trials (cRCTs).
Traditional fixed cRCTs face power limitations due to recruitment challenges.
Adaptive designs offer potential efficiency improvements over fixed designs.

Purpose of the Study:

To evaluate the feasibility of adaptive designs for implementation cRCTs with a small number of clusters.
To assess operating characteristics and adaptive interim decision-making in simulated adaptive cRCTs.
To compare the performance of adaptive versus fixed designs regarding power and type I error.

Main Methods:

A simulation study of a four-arm cluster randomized control trial (cRCT) was conducted.
Trials were simulated using both fixed and adaptive design parameters, including interim analyses and arm-dropping rules.
Bayesian hierarchical models were employed to analyze simulated data, comparing power and type I error rates.

Main Results:

Adaptive designs demonstrated small power gains without increasing type I error compared to fixed designs.
Power gains were diminished with high intra-class correlation (ICC) and low sample size.
High ICC increased the likelihood of incorrectly dropping the most effective arm in adaptive designs.

Conclusions:

Adaptive designs are feasible for implementation cRCTs with few clusters.
Feasibility is compromised in the presence of high ICC, increasing the risk of erroneous adaptive decisions.
Careful consideration of ICC is necessary when implementing adaptive designs in resource-limited settings.