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

Abstract

This article introduces the stepped-wedge design for social and health intervention research, focusing on statistical analysis with mixed-effects modeling to account for inherent clustering. We use data from 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 are from the INSTTEPP trial to evaluate impact of a bootcamp translation intervention on self-management support in primary care. We used these data 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. We then demonstrated data analysis using statistical mixedeffects modeling to incorporate the within-and between-cluster variation in a typical stepped-wedge study. Conclusions: This article demonstrates the potential of steppedwedge cluster design for improving interventions. Social work researchers can use the guidelines provided in this paper to design and analyze interventions, improving the effectiveness of interventions and strategies to achieve desired outcomes.