bamlss: simSurv – R documentation

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simSurv

Simulate Survival Times

Description

Function simSurv() and rSurvtime2() simulate arbitrary (Cox-type) survival times using the inversion method. Function simSurv() is a simple wrapper that calls rSurvtime2(). The functions are based on the R package CoxFlexBoost implementation rSurvtime() and only slightly modify the code.

Usage

## Simulate a pre-specified survival times data set.
simSurv(n = 300)

## Simulate arbitrary survival times.
rSurvTime2(lambda, x, cens_fct, upper = 1000, ...,
  file = NULL, subdivisions = 1000)

Arguments

`n`	The number of individuals for which survival times should be simulated.
`lambda`	function. Baseline hazard lambda(t,x) where time must be first argument.
`x`	matrix. (Sampled) values for covariates (without time).
`cens_fct`	function. Function to compute (random) censoring.
`upper`	upper boundary of the interval the random survival times fall into.
`...`	further arguments to be passed to `lambda` or `cens_fct`.
`file`	character. name of the data file the generated data set should be stored into (e.g., "survtimes.RData") or NULL if the dataset should directly be returned in R.
`subdivisions`	The maximum number of subintervals for the integration.

Details

This is basically a slight modification according the computation of the integral, see the manual page of function rSurvtime() of package CoxFlexBoost for details.

Value

A data.frame consisting of the observed survival time (time), the non-censoring indicator (event) and further covariates x is returned. If file is specified, the data.frame is additionally stored on the disc.

References

Benjamin Hofner (2016). CoxFlexBoost: Boosting Flexible Cox Models (with Time-Varying Effects). R package version 0.7-0.

Ralph Bender and Thomas Augustin and Maria Blettner (2005), Generating Survival Times to Simulate Cox Proportional Hazards Models. Statistics in Medicine, 24, 1713-1723.

Examples

## The following shows the code of the
## wrapper function simSurv().
set.seed(111)
n <- 100
X <- matrix(NA, nrow = n, ncol = 3)
X[, 1] <- runif(n, -1, 1)
X[, 2] <- runif(n, -3, 3)
X[, 3] <- runif(n, -1, 1)

## Specify censoring function.
cens_fct <- function(time, mean_cens) {
  ## Censoring times are independent exponentially distributed.
  censor_time <- rexp(n = length(time), rate = 1 / mean_cens)
  event <- (time <= censor_time)
  t_obs <- apply(cbind(time, censor_time), 1, min)
  ## Return matrix of observed survival times and event indicator.
  return(cbind(t_obs, event))
}

## log(time) is the baseline hazard.
lambda <-  function(time, x) {
  exp(log(time) + 0.7 * x[1] + sin(x[2]) + sin(time * 2) * x[3])
}

## Simulate data with lambda() and cens_fct().
d <- rSurvTime2(lambda, X, cens_fct, mean_cens = 5)

bamlss

Bayesian Additive Models for Location, Scale, and Shape (and Beyond)

v1.1-3

GPL-2 | GPL-3

Authors

Nikolaus Umlauf [aut, cre] (<https://orcid.org/0000-0003-2160-9803>), Nadja Klein [aut] (<https://orcid.org/0000-0002-5072-5347>), Achim Zeileis [aut] (<https://orcid.org/0000-0003-0918-3766>), Meike Koehler [ctb], Thorsten Simon [aut] (<https://orcid.org/0000-0002-3778-7738>), Stanislaus Stadlmann [ctb]

Initial release

2021-01-25