An individually randomized group-treatment trial (IRGT) 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 change agent. This design is common in surgical trials, where each surgeon operates on multiple patients.14, 44 It is common in psychotherapy trials, where a therapist may treat multiple patients, either in groups or as individuals.3, 11, 53 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.31
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 change agent, whether in person or through a video or other virtual connection.34 There may or may not be a similar structure in the control condition, depending on the nature of the control condition.
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:
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 or a priori stratification would be useful strategies to protect against confounding.
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 change agent. This interaction creates the expectation that some level of intraclass correlation (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 change agents, the df available to estimate the ICC, or the component of variance associated with the group or change agent, will be limited. As for 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, often badly.4, 5, 33-35, 45-47, 50, 51
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 ICC in one condition, but not in the other. 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.
The recommended solution to these challenges is similar to the solution proposed for GRTs. It is important to employ a priori matching or a priori stratification 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.
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The remaining sections provide resources that will be helpful to investigators considering an individually randomized group-treatment trial, or partially clustered design.