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In cluster randomised trials the outcome for each patient cannot be assumed to be independent of that of any other patient since the patients within one cluster (practice) are more likely to have similar outcomes.
All main data analyses adhere to the intention-to-treat principle, quantifing the intervention effect both at the level of the cluster (practice), and at the level of the individual patient.
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Intra-cluster (practice) correlation coefficients will be determined and published for all primary outcome variables to assist future research.
The PROC GENMOD procedure in SAS version 9.1 was used because it allows specifying two levels of clustering (practice site and individual).
This assumption is violated in our case because people were sampled in clusters (practices).
Estimating a drop-out rate of about 20%, 306 participants will be recruited for randomisation, distributed across 24 clusters (practices).
Using 60 clusters (practices) we would need 524 participants to have the same power as an individually randomised trial.
For the primary endpoint a generalized multi-level model, that takes the randomized clusters (practices) as random effect into account, with antibiotic prescription rate at T2 as dependent and random group as independent variable, will be fitted to the data.
No significant cluster effects (practice, therapist or teacher) were found, except for enablement, where a practice clustering effect was found, so only these results are presented allowing for clustering.
Allocation was conducted at the cluster (general practice) level.
Modelling took into account clustering within practice and person by adjusting for intra-cluster correlations in using robust standard errors at the practice level.
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