Admissible multi-arm stepped-wedge cluster randomized trial designs

TL;DR

This paper develops methods for designing and analyzing multi-arm stepped-wedge cluster randomized trials, optimizing sample size and design to reduce costs and improve efficiency, demonstrated through a real-world occupational therapy trial.

Contribution

It introduces a novel approach for sample size calculation and design optimization in multi-arm stepped-wedge trials using linear mixed models.

Findings

Potential to reduce observations by up to 58% for fixed power

Design optimization balances cost and covariance considerations

Practical application demonstrated in occupational therapy trial

Abstract

Numerous publications have now addressed the principles of designing, analyzing, and reporting the results of, stepped-wedge cluster randomized trials. In contrast, there is little research available pertaining to the design and analysis of multi-arm stepped-wedge cluster randomized trials, utilized to evaluate the effectiveness of multiple experimental interventions. In this paper, we address this by explaining how the required sample size in these multi-arm trials can be ascertained when data are to be analyzed using a linear mixed model. We then go on to describe how the design of such trials can be optimized to balance between minimizing the cost of the trial, and minimizing some function of the covariance matrix of the treatment effect estimates. Using a recently commenced trial that will evaluate the effectiveness of sensor monitoring in an occupational therapy rehabilitation program for older persons after hip fracture as an example, we demonstrate that our designs could reduce the number of observations required for a fixed power level by up to 58%. Consequently, when logistical constraints permit the utilization of any one of a range of possible multi-arm stepped-wedge cluster randomized trial designs, researchers should consider employing our approach to optimize their trials efficiency.