Abstract Simulation Class for Survival Data
Usage
SimSurvival(
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),
loghazard,
name = "SimSurvival"
)Arguments
- 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.- loghazard
(
function)
the log hazard function.- name
(
character)
the name of the object.
Slots
time_max(
numeric)
See arguments.time_step(
numeric)
See arguments.lambda_censor(
numeric)
See arguments.beta_cont(
numeric)
See arguments.beta_cat(
numeric)
See arguments.loghazard(
function)
See arguments.name(
character)
See arguments.
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:
SimSurvivalExponential(),
SimSurvivalGamma(),
SimSurvivalLogLogistic(),
SimSurvivalWeibullPH()