Table Of Content

SWTs randomly allocate clusters to groups that cross over from a control condition to an intervention at different crossover points (b). Key aspects of the allocation strategy are the number of clusters per group (d), the number of groups (e), and the length of time between successive crossover points, sometimes referred to informally as the ‘step length’ (h), which together also determine the total number of clusters (f) and total trial duration (a). We define a step in the design to be both a crossover point and the time to the subsequent crossover point (c). We developed a framework to define and report the key characteristics of a stepped wedge trial, including cluster allocation and individual participation. We also considered the relative strengths and weaknesses of trials according to this framework.
Power Calculations for Stepped Wedge Designs with Binary Outcomes: Methods and Software
Patrick Heagerty Archives - Rethinking Clinical Trials
Patrick Heagerty Archives.
Posted: Fri, 21 Jan 2022 15:44:59 GMT [source]
While the small sample makes generalisations difficult, the stepped wedge design appears to be primarily used in evaluating interventions in developing countries, with HIV the most common disease addressed (Table 1). Table 2 identifies that a number of different interventions were being evaluated, with vaccination, screening and education plans emerging as the most common interventions. Such interventions are likely to have an existing evidence base, adding to intuitive beliefs that the intervention is likely to do more good than harm. It is also possible that the use of the stepped wedge design is increasing, with 9 (75%) of the studies published since 2002. Dr. Xin Zhou is an Assistant Professor in the Department of Biostatistics at Yale School of Public Health.
Stepped Wedge Randomized Controlled Trials
The treatment effect can be optimally assessed under the assumption of an identical correlation at all time points. A method is available to calculate the power and the number of clusters that would be necessary in order to achieve statistical significance by the appropriate type of significance test. All of the statistical techniques presented here are based on the assumptions of a normal distribution of cluster means and of a constant intervention effect across all time points of measurement. Select summary of sample size methods for stepped wedge cluster randomized trials and related software.
Challenges

If statistical analysis of the trial is performed correctly (and at an appropriate level of complexity), basic methodological requirements can be met. Although the conditions required for a valid statistical evaluation of the treatment effect can be specified clearly in theory, in practice they are difficult to test. The basic principles of the stepped wedge design and related statistical techniques are described here on the basis of pertinent publications retrieved by a selective search in PubMed and in the CIS statistical literature database. In a cohort design, some acknowledgment for the dependence between individual measurements over the course of the study will be needed. The simplest option is perhaps to introduce an additional random effect for individuals in the study (as in example 3). The Matching Michigan study identified secular trends and no evidence of any intervention effect, even though at first the intervention looked to be a success.
Methods
September 5, 2023: NIH Pragmatic Trials Collaboratory Announces Grand Rounds Series on Design and Analysis of ... - Rethinking Clinical Trials
September 5, 2023: NIH Pragmatic Trials Collaboratory Announces Grand Rounds Series on Design and Analysis of ....
Posted: Tue, 05 Sep 2023 07:00:00 GMT [source]
However, interviewees recommended carefully weighing the advantages and challenges of SW-CRT design (Table 2) before selecting this design, given its numerous challenges, because deviations from the study design might introduce bias into the analyses. We believe that a well-conducted SWT, in which participants experience only one condition and analysis appropriately takes account of period effects, provides strong evidence concerning the effectiveness of an intervention, and that this evidence will be far stronger than that from a non-randomised rollout. In our view, such a carefully designed and analysed SWT can in principle be as rigorous as a standard CRT, and deserves to be viewed as an experimental design rather than quasi-experimental.
Design choice three: randomisation method
Individuals visiting the clinic/ward once it has crossed-over to intervention will then contribute data to the intervention section of the wedge. The SW-CRT design has strict randomization requirements; all practices must be enrolled before randomization, and practices are assigned to staggered start dates. The Northwest cooperative learned from prior experience with SW-CRTs that sites often want to start sooner rather than later or would not join unless they received an early intervention. This was one reason why that cooperative chose the 2×2 factorial design, which allows all sites to begin the intervention at the same time. The North Carolina cooperative had a different experience, in which sites wanted to start later than when they were assigned, owing to staffing or EHR changes.
Authors and Affiliations
This feature tends to enhance the precision of the study compared with a simple parallel study if substantial cluster effects are present (that is, if the intra-cluster correlation is large). Current methodological literature focuses mainly on trials with cross-sectional data collection at discrete times, yet many recent stepped wedge trials do not follow this design. In this article, we present a typology to characterise the full range of stepped wedge designs, and offer guidance on several other design aspects. The 12 studies included in this review describe evaluations of a wide range of interventions, across different diseases in different settings. However the stepped wedge design appears to have found a niche for evaluating interventions in developing countries, specifically those concerned with HIV. There were few consistent motivations for employing a stepped wedge design or methods of data analysis across studies.
METHODS:
A major change in the method used to finance healthcare in Mexico was evaluated by a phased and random implementation.8 A randomised evaluation on such a large scale represents a major achievement in the robust evaluation of a public policy. The Harvard research team was tasked with an evaluation at the request of the Mexican Ministry of Health (in the expectation that if the intervention was successful it would survive any change of government). Seventy four clusters were matched in pairs so that one received the intervention and the other acted as control (as illustrated in fig 1b). In this particular case, an undertaking was made to make the intervention available to control clusters on completion of the study.
However, SWTs encompass a broad range of designs, and the methodological literature is lagging behind the growth in the conduct of SWTs. Much of the literature to date has focussed on a small range of SWT designs where data are collected from individuals at discrete time points, and individuals contribute one measurement during the study [2–5]. This may, for example, arise from cross-sectional sampling from all clusters just before each crossover point (whenever a group of clusters changes from control to intervention condition). However, most SWTs described in the recent literature do not follow this particular design [1].
Stepped wedge designs can be attractive to study intervention programs aiming to improve the delivery of patient care, especially when examining a small number of heterogeneous clusters. CB and RL designed the study, CB undertook the literature searching, data extraction and analysis and CB and RL drafted the manuscript. Cook and Campbell were possibly the first authors to consider the potential for experimentally staged introduction in a situation when an innovation cannot be delivered concurrently to all units [3].
As the number and type of undetected cases will likely affect response to intervention there can be carry-over effects, most clearly in a closed cohort but also in an open cohort, unless individuals leave and join clusters at a high rate. This may be a concern in trials, such as one addressing detection and improved management of patients with multiple comorbidities and medications found in our review [25], or another trial involving identifying and treating depression in nursing homes [26]. This problem of changing participant distribution over time is most obvious for time-to-event outcomes such as death, and analysis of the intervention effect will be subject to survivor bias. Few (or even no) individuals participate as the trial begins, but more become eligible and participate over time, and are then exposed for a short period. The middle participant is exposed only to the control condition, although the outcome is recorded after the cluster has crossed over to the intervention condition.