Simulate Data from Cox Cure Model with Uncertain Event Status

Usage

simData4cure(
  nSubject = 1000,
  shape = 2,
  scale = 0.1,
  lambda_censor = 1,
  max_censor = Inf,
  p1 = 0.9,
  p2 = 0.9,
  p3 = 0.9,
  survMat,
  cureMat = survMat,
  b0 = stats::binomial()$linkfun(0.7),
  survCoef = rep(1, ncol(survMat)),
  cureCoef = rep(1, ncol(cureMat)),
  ...
)

Arguments

nSubject: A positive integer specifying number of subjects.
shape: A positive number specifying the shape parameter of the distribution of the event times.
scale: A positive number specifying the scale parameter of the distribution of the event times.
lambda_censor: A positive number specifying the rate parameter of the exponential distribution for generating censoring times.
max_censor: A positive number specifying the largest censoring time.
p1: A number between 0 and 1 specifying the probability of simulating events with observed event indicators given the simulated event times.
p2: A number between 0 and 1 specifying the probability of simulating susceptible censoring times with observed event status given the simulated susceptible censoring times.
p3: A number between 0 and 1 specifying the probability of simulating cured censoring times with observed event status given the simulated cured censoring times.
survMat: A numeric matrix representing the design matrix of the survival model part.
cureMat: A numeric matrix representing the design matrix excluding intercept of the cure rate model part.
b0: A number representing the intercept term for the cure rate model part.
survCoef: A numeric vector for the covariate coefficients of the survival model part.
cureCoef: A numeric vector for the covariate coefficients of the cure model part.
...: Other arguments not used currently.

Value

A data.frame with the following columns:

obs_time: Observed event/survival times.
obs_event: Observed event status.
event_time: Underlying true event times.
censor_time: underlying true censoring times.
oracle_event: underlying true event indicators.
oracle_cure: underlying true cure indicators.
case: underlying true case labels.

References

Wang, W., Luo, C., Aseltine, R. H., Wang, F., Yan, J., & Chen, K. (2023). Survival Modeling of Suicide Risk with Rare and Uncertain Diagnoses. Statistics in Biosciences, 17(1), 1–27.

Examples

## see examples of function cox_cure()