Two-stage Adaptive Design Cluster Randomised Trials

TL;DR

This paper develops adaptive design methods for cluster randomised trials, enabling early stopping, re-design, and efficient sample size estimation to reduce costs and improve trial flexibility.

Contribution

It introduces a combination test approach for adaptive cluster trials, accounting for correlations and multi-dimensional sample size decisions, with a Pareto optimality framework.

Findings

Effective early stopping for futility or efficacy demonstrated.

Methods applied to stepped-wedge trial re-design and E-MOTIVE re-analysis.

Enhanced efficiency and flexibility in cluster trial design.

Abstract

Adaptive sample size re-estimation, early stopping, and trial re-design at interim analyses can reduce expected sample sizes in randomised trials. Cluster randomised trials, in which groups of participants are randomly allocated to treatment status, may particularly benefit as they can be costly and their required sample sizes depend on one or more auxiliary parameters governing correlations within and between clusters, which are often estimated with high uncertainty. We adapt a combination test approach to the cluster trial setting allowing for early stopping for futility or efficacy and accounting for correlations between trial stages and other nuisance parameters. We consider design decisions for multi-dimensional sample sizes involving clusters, participants, and time and allowing for modifications to intervention roll-out patterns. We use a Pareto optimality approach to balance objectives relating to different components of the sample size and costs. We also examine the interim estimation of auxiliary parameters and trial re-design for efficiency. We illustrate the methods including examples of stepped-wedge trial re-design and a re-analysis of the large cluster randomised trial E-MOTIVE.