Primary aim
REGRESSION
The primary aim analyses will compare the main effects of the two types of longitudinal notification routings (A vs. B) on our primary outcome, the proportion of patients who have their DOAC prescription changed within 7 days. Mixed-effects logistic regression models will be used to model the probability of changed prescription, with fixed effects for notification type (A vs. B) and stratification variables (resident vs. non, primary care vs. specialist), and any patient-level characteristics that are unbalanced. A prescriber-level random effect will account for patient clustering within prescribers, and an unstructured covariance matrix will be used for residual errors. While we anticipate that few patients will appear in the data multiple times over the 18-month trial period, a patient-level random effect will also be considered, as appropriate.