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Accounting for within-group correlation
In the current issue of Injury Prevention, there are two studies of groups. Ozanne-Smith and colleagues studied the impact of an educational injury prevention program on two communities in Australia,1 and Lindqvist et al conducted a similar study in Sweden.2
Studies of naturally occurring groups are common for several reasons. For example, Levy and colleagues, in the United States, wanted to know if differences in state rules regarding driver license renewal were associated with the crash mortality rates of elderly drivers.3 Their study compared mortality rates in different states, because the exposure, licensing rules, applied to everyone in each state. Kannus et al, in Finland, wanted to study the association of hip protectors with hip fracture.4 They feared that if they randomized elderly individuals, it might be hard to keep those assigned to the control group from using the hip protectors, and this might attenuate the study's ability to detect any difference in outcomes. Therefore, they randomized treatment centers, rather than individuals within each center; the goal was to have all patients in some centers wear hip protectors while all patients in control centers would go without these devices. Other examples of groups in studies include students in schools,5 patients in physician practices,6 and workers in stores.7
A few textbooks and many articles have commented upon design and statistical issues that apply to studies of groups.8–15 In this commentary we will discuss one feature of group studies: individuals within groups may be more alike in their propensity to have a study outcome compared with individuals between groups.
When individuals are in groups (clusters), investigators need to consider the possibility that children in a classroom, patients in a practice, players on a baseball team, or citizens …