2.2 Discrete event simulation model
A discrete event simulation (DES) model was developed and reprogrammed in Python (version 3.7). The hypothetical cohorts of patients were generated according to the baseline covariate distributions that matched the marginal distribution of covariates reported in ROCKET AF, XANTUS, and the two observational studies (Laliberte 2014 and Amin 2017), respectively, using Monte Carlo simulation by randomly sampling. The random sampling continued until 7000 patients were simulated for each treatment group, which was similar to the sample size of ROCKET AF. Figure 1 presents the DES model structure built based on a priori knowledge about disease progression and possible outcomes of AF patients receiving rivaroxaban3, 13. The model was designed to predict treatment outcomes conditional on patients’ baseline characteristics. CHADS2 score of individual patients was calculated according to the simulated patient’s baseline characteristics. Therefore, patients at different stroke risk would trace different probabilistic pathways in the model based on their treatment assignment (rivaroxaban or warfarin). The incidence of the events in the simulation model was obtained based on the rates reported in ROCKET AF (Table S2) 5, 14-18. Cardiovascular events, such as stroke/SE, major bleeding (GI bleeding, ICH and other major bleeding), nonmajor clinically relevant bleeding (NMCR bleeding), MI, and unknown death, were recorded during the simulated two-year follow-up period. The patient’s cardiovascular profile was updated from the first year to the second year. Patients who suffered death, fatal stroke/SE, fatal major bleeding, or fatal MI, were supposed to exit the simulation model after their outcomes were recorded. The event rates, hazard ratios (HRs), and risk differences (RDs) were calculated and reported.