## 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 0.926591689 1 0.001893297 1 0.001893297 1 0
## 2 0.003151729 1 0.315942026 1 0.003151729 1 0
## 3 1.000000000 0 0.326341426 1 0.326341426 1 0
## 4 0.058951845 1 0.041262698 1 0.041262698 1 0
## 5 0.773331231 1 0.095865371 1 0.095865371 1 0
## 6 0.031138715 1 0.099793426 1 0.031138715 1 0