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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.
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.
To handle this potential bias, we have used a random intercept at centre level in our linear multilevel model.
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We used a random intercepts multilevel model to control for evidence of clustering in the data by allowing the overall rate of provision relative to need to vary across wards and districts.
This model accounts for the repeated responses from the same circuit using a random intercept.
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.
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