itsamp: Inverse Transform Sampling To Generate Time-to-event Data From Parametric Models

Description

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

Usage

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
)

Arguments

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

Value

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.

Details

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.

Examples

Run this code

# NOT RUN {
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')

# }