## Probabilities of observing the event in control arm during follow-up
p0_e1 <- 0.59 # Death
p0_e2 <- 0.74 # Disease Progression
## Effect size (Cause specific hazard ratios) for each endpoint
HR_e1 <- 0.91 # Death
HR_e2 <- 0.77 # Disease Progression
## Hazard rates over time
beta_e1 <- 2 # Death --> Increasing risk over time
beta_e2 <- 1 # Disease Progression --> Constant risk over time
## Correlation
rho <- 0.1 # Correlation between components
rho_type <- 'Spearman' # Type of correlation measure
copula <- 'Frank' # Copula used to get the joint distribution
## Additional parameter
case <- 3 # 1: No deaths; 2: Death is the secondary event;
# 3: Death is the primary event; 4: Both events are death by different causes
## Sample size
sample_size <- 100
## Effect size for each endpoint
eff_e1 <- -0.0196
eff_e2 <- -0.0098simulation_timetoevent<- simula_tte(p0_e1,
p0_e2,
HR_e1,
HR_e2,
beta_e1 = 1,
beta_e2 = 1,
case,
copula = "Frank",
rho = 0.3,
rho_type = "Spearman",
followup_time = 1,
sample_size)## time_e1 status_e1 time_e2 status_e2 time_ce status_ce treated
## 1 0.9312005 1 0.3016660 1 0.3016660 1 0
## 2 1.0000000 0 0.4564353 1 0.4564353 1 0
## 3 1.0000000 0 0.7926898 1 0.7926898 1 0
## 4 1.0000000 0 0.7923706 1 0.7923706 1 0
## 5 0.1146371 1 0.3436243 1 0.1146371 1 0
## 6 1.0000000 0 0.5244142 1 0.5244142 1 0