itsamp: Inverse Transform Sampling To Generate Time-to-event Data...
In spsurv: Bernstein Polynomial Based Semiparametric Survival Analysis

Description Usage Arguments Details Value See Also Examples

Random survival times generation for the weibull or log-logistic distributions with parameters 'scale' and 'shape'.

itsamp(
  n,
  beta = c(2, -1),
  event_scale = 10,
  censor_scale = 4,
  features = data.frame(x1 = rnorm(n, 0), x2 = rnorm(n, 0)),
  shape = 2,
  model = c("ph", "po", "aft"),
  dist = c("weibull", "llogis"),
  censor = TRUE
)

`n`	integer; sample size
`beta`	vector of regression coefficients
`event_scale, censor_scale`	event and censoring scale parameters
`features`	matrix of features (columns)
`shape`	event and censoring distribution shape
`model`	either "ph" (default) or "aft" for weibull and "po" or "aft" for log-logistic distribution
`dist`	"weibull" or "llogis"
`censor`	logical; if 'TRUE', censoring is required, that is mean(status) > 0

sim_surv returns weibull (log-logistic) randomly generated survival times. According to Collett (2003), the accelerated failure time model encompasses a wide variety of parametric models, including weibull and log-logistic models.

data.frame of 'ncol(x) +2' columns in which the survival times are the response variable denoted by 'y', 'status' indicates failure (0 = failure) and the features are appended to the next columns.

spbp

rows <- 200

categorical <- rbinom(rows, size = 3, prob = .5)
x <- data.frame(numerical = rnorm(rows),
           cat0 = as.numeric(categorical == 0),
           cat1 = as.numeric(categorical == 1),
           cat2 = as.numeric(categorical == 2),
           cat3 = as.numeric(categorical == 3))

newdata <- itsamp(n = rows, beta = c(1, -2, .5, .1, 1),
  features = x, model = 'ph', dist = 'weibull')