Stepped Wedge Cluster Randomized Trials: A Methodological Overview PMC

Table Of Content

• Authors’ original file for figure 2
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• Power Calculations for Stepped Wedge Designs with Binary Outcomes: Methods and Software
• STATISTICAL CONSIDERATIONS FOR STEPPED WEDGE DESIGNS
• Overview of Statistical Models for the Design and Analysis of Stepped Wedge Cluster Randomized Trials

A critical consideration in using existing tools for sample size calculation is the assumptions on unknown ICCs. It has been encouraged in the CRT and SW-CRT literature7071 to report ICCs to facilitate the design of future trials with similar endpoints. By providing key conceptual and analytical considerations, we aspire to encourage researchers to evaluate the potential for adopting a stepped wedge design in their study and thereby help with generating high-quality treatment effect evidence for patient care. When outcomes are not based on routinely collected data or when individual recruitment is required, as in all cluster trials, special consideration should be given to minimising selection biases. Incomplete designs have been proposed in which data are not collected from all clusters at all times.

Authors’ original file for figure 2

In other words, one cannot conclude from the trial data that the intervention has a positive effect on patients’ physical quality of life. Proforma used to extract data from the included papers or protocols prior to generating a database from these data. Where only one figure for the number of participants is given, each individual/household participant receives the intervention at some stage during the trial.

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These groups are generally—as well as in the rest of this article—referred to as clusters. Considering the scientific advantages of the stepped wedge design, it has rarely been used in practice and hence we advocate the design for evaluating a wide range of interventions, although we are not the first to do so [26-28]. The conduct of the stepped wedge cluster randomised trial bears much in common with the main alternatives—the parallel cluster trial and the parallel cluster randomised trial with a baseline period. Since all these designs are used to study similar policy and service delivery interventions, they raise many of the same issues, particularly those relating to selection and concealment.

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April 10, 2023: Li Receives New PCORI Award to Develop Causal Inference Methods for Stepped-Wedge Cluster ... - Rethinking Clinical Trials

April 10, 2023: Li Receives New PCORI Award to Develop Causal Inference Methods for Stepped-Wedge Cluster ....

Posted: Mon, 10 Apr 2023 07:00:00 GMT [source]

In case study three there seems to have been no constraint on the number of steps, as data collection occurred at time points unrelated to steps. There was an implementation lag as staff training was required, but once training was received there seems no reason for a further delay for the intervention to affect the outcome of hand hygiene compliance. The two studies that apply step-by-step analysis [4,13] provide a separate analysis for each step in the trial, in order to separate out underlying time trends.

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We classified recently published stepped wedge trials using this framework and identified illustrative case studies. This extended procedure was used to obtain the findings shown in Box 2 by analyzing the sample SWD trial described in Table 3. Full details of the procedure can be found in the documentation relating to software programs for analyzing mixed linear models such as the SAS PROC MIXED Procedure (9). Such complex statistical models should also be used to analyze trials in which correlations between repeated measurements are assumed to be due to intraindividual effects. Among others, this this typically implies that the variation between clusters cannot be described any longer in accordance with Condition 2 by a single dispersion parameter.

Whilst the clusters in an SWT normally participate throughout the trial, experiencing control and intervention conditions at different times according to the allocation strategy, the ways in which individuals are exposed and participate vary greatly between trials. For example, in some SWTs, all individuals participate in the trial from start to end and experience both control and intervention conditions. These features are often outside the control of the trialists, but influence how SWTs are designed. In other SWTs where large clusters (such as cities) are randomised, then only a small fraction of the participants may be invited to provide outcome measurements, for example by a questionnaire survey. Stepped wedge cluster randomised trials (SWTs) are becoming increasing popular and are being applied to a growing range of interventions, as shown in our review article [1].

Overview of Statistical Models for the Design and Analysis of Stepped Wedge Cluster Randomized Trials

In 2007 Hussey and Hughes4 first described methods to determine statistical power available when using a stepped wedge design. However, there is a dearth of literature on the more general methodological aspects, such as the rationale for, and conduct of, stepped wedge studies. In this article we illustrate how this new study design differs from the conventional parallel design and its variations. We also give several examples and consider several design and methodological issues, including rationale, sample size, and efficiency compared with competing designs, and highlight some important reporting and analysis considerations. Considering the scientific advantages of the stepped wedge design, it has rarely been used in practice and hence we advocate the design for evaluating a wide range of interventions, although we are not the first to do so [26–28]. The stepped wedge design may also be appropriate for cost-effectiveness analyses of interventions that have already been shown to be effective.

In such circumstances, the alternative to a stepped wedge design may not be a parallel cluster trial but a weaker, non-experimental design. Under such a scenario the stepped wedge design is “naturalistic” in that the implementation may proceed much as it would have done had the evaluation not been in place while allowing randomised evidence of effectiveness. In the preceding section we have seen that a complete SWT may be of longer duration or fewer steps than wished, because a long step length is selected due to a lag period.

This process continues until all clusters have crossed over to be exposed to the intervention. Data collection continues throughout the study, so that each cluster contributes observations under both control and intervention observation periods. It is a pragmatic study design, giving great potential for robust scientific evaluations that might otherwise not be possible. The stepped wedge cluster randomized design has received increasing attention in pragmatic clinical trials and implementation science research.

Though substantial carry-over effects are uncommon in stepped wedge trials, researchers should consider their possibility before conducting a trial in which individuals experience both control and intervention conditions, such as a closed or open cohort trial. Stepped wedge randomised trial designs involve sequential roll-out of an intervention to participants (individuals or clusters) over a number of time periods. By the end of the study, all participants will have received the intervention, although the order in which participants receive the intervention is determined at random. Stepped wedge designs offer a number of opportunities for data analysis, particularly for modelling the effect of time on the effectiveness of an intervention.