Abstract
In stepped‐wedge trials (SWTs), the intervention is rolled out in a random order over more than 1 time‐period. SWTs are often analysed using mixed‐effects models that require strong assumptions and may be inappropriate when the number of clusters is small.We propose a non‐parametric within‐period method to analyse SWTs. This method estimates the intervention effect by comparing intervention and control conditions in a given period using cluster‐level data corresponding to exposure. The within‐period intervention effects are combined with an inverse‐variance‐weighted average, and permutation tests are used. We present an example and, using simulated data, compared the method to (1) a parametric cluster‐level within‐period method, (2) the most commonly used mixed‐effects model, and (3) a more flexible mixed‐effects model. We simulated scenarios where period effects were common to all clusters, and when they varied according to a distribution informed by routinely collected health data.The non‐parametric within‐period method provided unbiased intervention effect estimates with correct confidence‐interval coverage for all scenarios. The parametric within‐period method produced confidence intervals with low coverage for most scenarios. The mixed‐effects models' confidence intervals had low coverage when period effects varied between clusters but had greater power than the non‐parametric within‐period method when period effects were common to all clusters.The non‐parametric within‐period method is a robust method for analysing SWT. The method could be used by trial statisticians who want to emphasise that the SWT is a randomised trial, in the common position of being uncertain about whether data will meet the assumptions necessary for mixed‐effect models.
Highlights
Parallel cluster‐randomised trials (CRTs) with sufficient numbers of clusters can be analysed in 2 ways: at the cluster level using cluster‐level summaries of observation‐level data, or by accounting for correlation in the observation‐level data using statistical models
We focused on a binary outcome, which is common in Stepped‐wedge trials (SWTs).[7]
As with the non‐parametric within‐period method, we focus on estimation of an odds ratio, Yij = log (pij/(1 − pij)), but other intervention effects can be calculated, and other outcome types can be analysed in a similar way
Summary
Parallel cluster‐randomised trials (CRTs) with sufficient numbers of clusters can be analysed in 2 ways: at the cluster level using cluster‐level summaries of observation‐level data, or by accounting for correlation in the observation‐level data using statistical models. Between‐period comparisons are confounded with changes in participation and in the outcome over time (secular trends or “period effects”) because each cluster is in the intervention condition later than the control condition.[5] Analyses that incorporate between‐period comparisons must adjust for period effects and must make assumptions about the correlation of observations within each cluster. Using simulations based on routinely collected data, we assessed the performance of the methods in terms of bias, coverage, and power
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