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This model accounts for the repeated responses from the same circuit using a random intercept.
Data were collected from several different centers, and to account for potential cluster effects, we therefore used a random intercept model.
We used a random intercept to represent variations between calibration sources since we considered them to be a random sample of calibration sources and were not interested in the specific effect of each source.
We used a random intercept and random slopes model.
Patients were nested within GP practice using a random intercept.
The models were further adjusted for residual clustering within areas using a random intercept.
We determined bivariate associations with Hgb A1c as outcome using a random intercept linear mixed model.
We used a random intercept and slope (slope versus time) model in all cases.
We used a random intercept to allow for spatial correlation of neighboring suburbs.
This model accounts for the repeated responses from the same circuit by using a random intercept.
We adjusted for clustering at the GP level by using a random intercept.
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