Stepped-Wedge Cluster Randomized Control Trial for Social Intervention Research: Design and Analysis

Background and Purpose: Social interventions are strategies purposefully designed and tested to improve social and health wellbeing. In intervention design, the randomized controlled trial (RCT) has long been considered the “Gold Standard” for evaluating the effectiveness of interventions, representing a best practice for identifying the causal effect of intervention on participant outcomes. Conventionally, individuals in RCTs are randomly allocated to either an intervention or a control/comparison condition; however, for some intervention studies it is impractical or unethical to randomize participants at the individual level because of two primary challenges. First, many interventions result from policies aimed at regional systems or specific populations. Consequently, researchers cannot withhold the intervention from members of the policy-intended population in an effort to construct a proper counterfactual. All members of the intended population must be offered the intervention, though perhaps not at the same time. In such situations, staggering introduction to the intervention can preserve the benefits of random assignment while offering intervention to all members of the population within a timeframe. Second, randomly assigning groups of individuals within natural clusters can be preferable to randomly assigning individuals within a cluster to separate intervention conditions when concerns about intervention spillover and delivery are relevant. Random assignment of clusters of individuals can result in correlations among study participants that violate the assumptions of treatment estimation techniques, for example ordinary least squares regression, and might ultimately confound the estimation of the causal effect of intervention on outcomes. In those situations, an alternative approach is to randomize entire groups of participants (clusters) to the two conditions. One design that can address both challenges described above is the stepped-wedge cluster randomized controlled trial. In this design, the intervention is administered in a random sequence to all clusters of participants over a number of time “steps”, thus ensuring persons intended to be served are offered intervention and the benefits of random assignment for causal estimation are preserved. This presentation discusses the stepped-wedge design for social intervention research to account for inherent clustering. We make use of data from the Implementing Networks’ Self-Management Tools through Engaging Patients and Practices (INSTTEPP) to illustrate the utility of the stepped-wedge design and provide detailed step-by-step implementation for conceptualizing stepped-wedge studies and analyzing data to identify treatment effects.

Method: Summary data reported on the INSTTEPP trial to evaluate impact of a bootcamp translation intervention on self-management support in primary care were utilized as reference trial settings to generate simulated data for illustration purposes.

Results: Using summary parameters from the INSTTEPP trial, we simulated individual-level data for 320 participants in 16 clusters within a stepped-wedge design, and we then demonstrated data analysis using statistical mixed-effects modelling to incorporate the within and between cluster variation in a typical stepped-wedge study.

Conclusions and Implications: This presentation demonstrates the capacity of the stepped-wedge cluster design for improving interventions. Social work researchers can use the guidelines provided in this paper to design and analyze using the stepped-wedge for improving the effectiveness of interventions and strategies to achieve desired outcomes.