Design optimisation and post-trial analysis in group sequential stepped-wedge cluster randomised trials

TL;DR

This paper introduces optimized group sequential design methods for stepped-wedge cluster randomised trials, improving efficiency and bias reduction while maintaining accurate statistical inference.

Contribution

It extends previous methods by detailing how to optimize stopping boundaries, allocation, and sample sizes, and how to accurately analyze data accounting for the sequential design.

Findings

Up to 30% reduction in expected measurements under the null hypothesis.

Almost universal bias reduction in point estimates.

Confidence intervals maintain coverage close to nominal levels.

Abstract

Recently, methodology was presented to facilitate the incorporation of interim analyses in stepped-wedge (SW) cluster randomised trials (CRTs). Here, we extend this previous discussion. We detail how the stopping boundaries, allocation sequences, and per-cluster per-period sample size of a group sequential SW-CRT can be optimised. We then describe methods by which point estimates, p-values, and confidence intervals, which account for the sequential nature of the design, can be calculated. We demonstrate that optimal sequential designs can reduce the expected required number of measurements under the null hypothesis, compared to the classical design, by up to 30%, with no cost to the maximal possible required number of measurements. Furthermore, the adjusted analysis procedure almost universally reduces the average bias in the point estimate, and consistently provides a confidence interval with coverage close to the nominal level. In contrast, the coverage of a naive 95% confidence interval is observed to range between 92 and 98%. Methodology is now readily available for the efficient design and analysis of group sequential SW-CRTs. In scenarios in which there are substantial ethical or financial reasons to terminate a SW-CRT as soon as possible, trialists should strongly consider a group sequential approach.