simulate_survival_cox: Simulating survival data
In SurvBoost: Gradient Boosting for Survival Data

Description Usage Arguments Value Examples

View source: R/simulate_survival_cox.R

This function allows you to simulate stratified survival data.

simulate_survival_cox(true_beta, base_hazard = "auto",
  base_hazard_scale = NULL, base_hazard_shape = NULL,
  num_strata = 10, input_strata_size = 50, z_matrix = NULL,
  cov_structure = "diag", block_size = 1, rho = NULL,
  censor_dist = "unif", censor_const = 5, tau = Inf,
  normalized = F)

`true_beta`	Contains true parameter values to simulate from.
`base_hazard`	Baseline hazard distribution. Default is set to exponential, "weibull" is also available.
`base_hazard_scale`	Scale parameter used if baseline hazard distribution is weibull.
`base_hazard_shape`	Shape parameter used if baseline hazard distribution is weibull.
`num_strata`	Number of strata to simulate, default is 10.
`input_strata_size`	Average sample size of each stratum, default is 50.
`z_matrix`	Covariate matrix. Default is NULL, will be simulated as multivariate normal if not provided.
`cov_structure`	Covariance structure. Default is "diag" could also be "ar" for AR1 or "cs" for compound symmetry.
`block_size`	Block size used for covariance structure. Default value is 1.
`rho`	Correlation parameter used for "ar" or "cs" covariance structure.
`censor_dist`	Censoring distribution, default is "unif" for uniform distribution. Exponential distribution is used if set to "exp"
`censor_const`	Parameter used to specify the censoring distribution. Default value is 5.
`tau`	Positive scalar used to represent possible follow up time. Default is Inf.
`normalized`	Logical parameter representing whether or not the covariate matrix should be normalized. Default is FALSE.

a matrix with survival time (time), event indicator (delta), stratification variable (strata_idx), a vector for each variable specified by the true_beta.

toyData <- simulate_survival_cox(true_beta=c(1,1,1,1,1,0,0,0,0,0), 
base_hazard="weibull", base_hazard_scale=rep(1,5), base_hazard_shape=rep(2,5), 
num_strata=5, input_strata_size=100, cov_structure="diag", block_size=2, 
rho=0.3, censor_dist="unif", censor_const=5, tau=Inf, normalized=FALSE)
any(duplicated(toyData$time))
z <- as.matrix(toyData[,-c(1,2,3)])