Simulate Survival Data from a Log-Logistic Proportional Hazard Model

Simulate Survival Data from a Log-Logistic Proportional Hazard Model

Usage

SimSurvivalLogLogistic(
  a,
  b,
  time_max = 2000,
  time_step = 1,
  lambda_censor = 1/3000,
  beta_cont = 0.2,
  beta_cat = c(A = 0, B = -0.4, C = 0.2)
)

Arguments

a

(number)
the scale parameter.

b

(number)
the shape parameter.

time_max

(number)
the maximum time to simulate to.

time_step

(number)
the time interval between evaluating the log-hazard function.

lambda_censor

(number)
the censoring rate.

beta_cont

(number)
the continuous covariate coefficient.

beta_cat

(numeric)
the categorical covariate coefficients.

Hazard Evaluation

Event times are simulated by sampling a cumulative hazard limit from a \(U(0, 1)\) distribution for each subject and then counting how much hazard they've been exposed to by evaluating the log-hazard function at a set interval. The time_max argument sets the upper bound for the number of time points to evaluate the log-hazard function at with subjects who have not had an event being censored at time_max. The time_step argument sets the interval at which to evaluate the log-hazard function. Setting smaller values for time_step will increase the precision of the simulation at the cost of increased computation time. Likewise, setting large values for time_max will minimize the number of censored subjects at the cost of increased computation time.

See also

Other SimSurvival: SimSurvival-class, SimSurvivalExponential(), SimSurvivalGamma(), SimSurvivalWeibullPH()