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General linear mixed models (GLMM) were used to analyze differences between these groups or clusters [ 43, 44] with the covariance structure appropriately adjusting for variability both between cluster and also within cluster [ 36, 45].
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Significant differences among risk factors were observed between cluster and non-cluster areas.
General linear mixed models were used to analyze the differences between intervention and control schools (6, 7), with the covariance structure appropriately adjusting for variability both between cluster (school) and within cluster (students within the same school) (8, 9).
To control for differences in patient-day and ward characteristics among intervention and control arms, we adjusted for variability both between clusters (wards) and within a cluster (patients within the same ward), and controlled for patient and ward covariates.
However, the estimated cluster means incorporate both within and between-cluster variability and any analysis that restricts itself to the cluster means will over-estimate the variance in these means [ 7].
These modified structures were formed by freezing and thawing, along with interactions between clusters or between clusters and ice.
A second difference between clusters and bulk matter is the variability of the properties of clusters with the number of their constituent particles.
In addition, tradeoffs between clusters and individual facilities configurations are assessed.
Results suggested a reasonably good correspondence between clusters and their associated synoptic observation stations.
There was no association between clusters and bedside-derived hemodynamic profiles (p = 0.70).
The figure shows the association between clusters and states.
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