Random generation from Yang and Prentice models

Function to generate a random sample of survival data from Yang and Prentice models.

Usage

rypreg(u, formula, baseline, beta, phi, dist = NULL, data, ...)

Arguments

u: a numeric vector of quantiles.
formula: formula specifying the linear predictors.
baseline: the name of the baseline survival distribution.
beta: vector of short-term regression coefficients.
phi: vector of long-term regression coefficients.
dist: an alternative way to specify the baseline survival distribution.
data: data frame containing the covariates used to generate the survival times.
...: further arguments passed to other methods.

Value

a numeric vector containing the generated random sample.

Examples

# \donttest{
library(rsurv)
n <-  1000
simdata <- data.frame(
  age = rnorm(n),
  sex = sample(c("f", "m"), size = n, replace = TRUE)
) %>%
  mutate(
    t = rypreg(runif(n), ~ age+sex, beta = c(1, 2), phi = c(-1, 2),
                dist = "weibull", shape = 1.5, scale = 1),
    c = runif(n, 0, 10)
  ) %>%
  rowwise() %>%
  mutate(
    time = min(t, c),
    status = as.numeric(time == t)
  )
glimpse(simdata)
#> Rows: 1,000
#> Columns: 6
#> Rowwise: 
#> $ age    <dbl> -0.3194983, 0.6586742, -0.5764291, -0.1301443, 0.0477878, -0.52…
#> $ sex    <chr> "m", "m", "m", "m", "f", "m", "f", "m", "f", "f", "m", "m", "m"…
#> $ t      <dbl> 0.52929541, 0.26096194, 0.37880398, 0.53884157, 1.34703765, 0.2…
#> $ c      <dbl> 1.3665020, 5.1078673, 2.7427878, 6.1235070, 9.3289321, 4.584463…
#> $ time   <dbl> 0.52929541, 0.26096194, 0.37880398, 0.53884157, 1.34703765, 0.2…
#> $ status <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
# }