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In-Depth Information
Group Randomized Trials.
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Group randomized trials (GRTs) in public
health research typically use a small number of randomized groups with a
relatively large number of participants per group. Typically, some naturally
occurring groups are targeted: work sites, schools, clinics, neighborhoods,
even entire towns or states. A group can be assigned to either the inter-
vention or control arm but not both; thus, the group is nested within the
treatment. This contrasts with the approach used in multicenter clinical
trials, in which individuals within groups (treatment centers) may be
assigned to any treatment.
GRTs are characterized by a positive correlation of outcomes within a
group, along with a small number of groups. “There is positive intraclass
correlation (ICC) between the individuals' target-behavior outcomes
within the same group. This can be due in part to the differences in char-
acteristics between groups, to the interaction between individuals within
the same group, or (in the presence of interventions) to commonalities
of the intervention experienced by an entire group. Although the size of
the ICC in GRTs is usually very small (e.g., in the Working Well Trial,
between 0.01 and 0.03 for the four outcome variables at baseline), its
impact on the design and analysis of GRTs is substantial.”
“The sampling variance for the average responses in a group is
(s
2
/
n
)*[1 + (
n
- 1)s)], and that for the treatment average with
k
groups
and
n
individuals per group is (s
2
/
n
)*[1 + (
n
- 1)s], not the traditional
s
2
/
n
and s
2
/(
nk
), respectively, for uncorrelated data.”
“The factor 1 + (
n
- 1)s is called the variance inflation factor (VIF), or
design effect. Although s in GRTs is usually quite small, the VIFs could
still be quite large because VIF is a function of the product of the correla-
tion an group size
n
.”
“For example, in the Working Well Trial, with s = 0.03 for daily
number of fruit and vegetable servings, and an average of 250 workers per
work site, VIF = 8.5. In the presence of this deceivingly small ICC, an
8.5-fold increase in the number of participants is required in order to
maintain the same statistical power as if there were no positive correlation.
Ignoring the VIF in the analysis would lead to incorrect results: variance
estimates for group averages that are too small.”
To be appropriate, an analysis method of GRTs need to acknowledge
both the ICC and the relatively small number of groups. Three primary
approaches are used (Table 5.2):
1.
Generalized Linear Mixed Models
(
GLMM
). This approach, imple-
mented in SAS Macro GLIMMIX and SAS PROC MIXED, relies
on an assumption of normality.
This section has been abstracted (with permission from
Annual Reviews
) from Feng et al.
[2001], from whom all quotes in this section are taken.
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