Random generation from proportional hazards models

Function to generate a random sample of survival data from proportional hazards models.

Usage

rphreg(u, formula, baseline, beta, dist = NULL, package = 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 regression coefficients.
dist: an alternative way to specify the baseline survival distribution.
package: the name of the package where the assumed quantile function is implemented.
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

library(rsurv)
library(dplyr)
n <-  1000
simdata <- data.frame(
  age = rnorm(n),
  sex = sample(c("f", "m"), size = n, replace = TRUE)
) %>%
  mutate(
    t = rphreg(runif(n), ~ age+sex, beta = 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> 0.20132441, -0.28838825, -1.20566309, -1.79280986, -1.09822670,…
#> $ sex    <chr> "m", "f", "m", "m", "m", "m", "f", "m", "m", "f", "f", "m", "f"…
#> $ t      <dbl> 0.23646815, 0.77732579, 0.09428336, 0.10775901, 0.86407215, 0.1…
#> $ c      <dbl> 2.861600, 9.481602, 9.357638, 1.442198, 8.562252, 4.103321, 4.7…
#> $ time   <dbl> 0.23646815, 0.77732579, 0.09428336, 0.10775901, 0.86407215, 0.1…
#> $ status <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …