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.