In an individually randomized group-treatment (IRGT) trial, also called a partially clustered or partially nested design, individuals are randomized to study conditions but receive at least some of their intervention with other participants or through an interventionist or facilitator shared with other participants (
Special methods are needed for analysis and sample size estimation for these studies, as detailed below and in the IRGT sample size calculator.
Features and Uses
Groups or Common Interventionist or Facilitator
An IRGT trial is a randomized trial in which participants in one or more study conditions receive at least some of their treatment in groups or through a common interventionist or facilitator. This design is common in surgical trials, where each surgeon operates on multiple patients (
- Methods: Mind the Gap Webinar: Design and Analysis of Individually Randomized Group-Treatment Trials in Public Health
- Pragmatic and Group-Randomized Trials in Public Health and Medicine Course
- Methods: Mind the Gap Webinar: Design and Analysis of Studies to Evaluate Multilevel Interventions in Public Health and Medicine
It is common in psychotherapy trials, where a therapist may treat multiple patients, either in groups or as individuals (; ; ). It is common in a variety of intervention trials addressing health behaviors such as weight loss, smoking cessation, and physical activity, which may include group activities as well as individual activities ( ).
Nested or Hierarchical Design
IRGTs always have a hierarchical structure in the intervention condition. Participants may receive some of their treatment in groups, or they may receive their intervention individually, but through a common interventionist or facilitator, whether in person or through a video or other virtual connection (). These issues are especially challenging in an IRGT because the design may not have the same hierarchical structure in all conditions. In that case, the analytic model must accommodate a heterogeneous variance-covariance structure, allowing for intraclass correlation (ICC) in the intervention condition, but not in the control condition. For this reason, it is even more important for investigators to rely on an experienced methodologist in developing design and analytic plans for an IRGT.
IRGTs can be employed in a wide variety of settings and populations to address a wide variety of research questions. They are an appropriate design when the investigator wants to evaluate an intervention that:
- involves at least one component that is delivered in a group format,
- it is necessary to use a limited number of intervention delivery staff, or interventionists or facilitators, so that each one interacts with multiple participants, or
- it is necessary to have participants interact with one another in a virtual environment.
Potential for Confounding
IRGTs randomize individuals to study conditions. If the number is large, confounding is not likely to be a threat to the internal validity of the design. If the number is small, confounding could be a threat, and a priori matching, a priori stratification, or constrained randomization would be useful strategies to protect against confounding.
Intraclass Correlation (ICC)
The more challenging feature of IRGTs is that participants in at least the intervention condition will interact post-randomization. Even if their groups or virtual networks are constructed using random assignment, those participants will interact with one another directly in their groups or indirectly through their common interventionist or facilitator. This interaction creates the expectation that some level of ICC will develop. The magnitude of the ICC in an IRGT will depend on the type, duration, and intensity of these interactions. That ICC may be negligible at baseline, but it can develop over the course of the trial. With a limited number of groups or interventionists or facilitators, the degrees of freedom (df) available to estimate the ICC, or the component of variance associated with the group or interventionist or facilitator, will be limited. As for group-randomized trials (GRTs), any analysis that ignores the extra variation (or positive ICC) or the limited df will have a type 1 error rate that is inflated. (; ; ; ; ; ; ; ; ; ).
The recommended solution to these challenges is like the solution proposed for GRTs. It is important to employ a priori matching, a priori stratification, or constrained randomization to balance potential confounders if the number of assignment units is limited, to reflect the hierarchical or partially hierarchical structure of the design in the analytic plan, and to estimate the sample size for the IRGT based on realistic and data-based estimates of the ICC and the other parameters indicated by the analytic plan. Extra variation and limited df always reduce power, so it is essential to consider these factors while the study is being planned, and particularly as part of the sample size estimation.
The sections below provide additional resources for investigators considering an individually randomized group-treatment trial, or partially clustered design.