Statistical Analysis
TIA, stroke
REGRESSION, STROKE, TIA
All results were reported using an intention-to-treat analysis. Baseline characteristics are summarized as the mean (SD) or median (IQR) for continuous variables and as numbers (percentages) for categorical variables. Baseline characteristics between the intervention and control participants were compared using crude differences and 95% CIs.We first performed the analyses for the primary analysis using mixed-effects logistic regression models with a random effect for the cluster (hospital) and a fixed-time effect for every 6-month period. However, the models did not converge. Therefore, we modeled the outcomes using generalized estimating equation models to account for the clustering effect within hospitals, with a fixed-time effect for the period. To check the robustness of our primary analysis, we also modeled the outcomes using generalized estimating equation models with additional adjustment for baseline imbalanced covariates (covariate-adjusted analysis), including medical history of stroke or TIA, medication history of antiplatelet agent and statin, and hospital location. In all these analyses, we reported the absolute effect measure risk difference (RD) and 95% CI estimated from a binomial regression model with the identity link function, in addition to the relative effect measure odds ratio (OR) and 95% CI.A total of 1423 of 11 821 participants discharged alive (12.0%) had missing data for mRS scores at 90 days; therefore, we first reported the results based on the complete data and then used multiple imputations by fully conditional specification to generate 5 complete data sets. The imputation models included all baseline covariates listed in 