# simSurv

##### Simulate Survival Times

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.

- Keywords
- distribution, survival

##### 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.

##### See Also

##### Examples

```
# NOT RUN {
## 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)
# }
```

*Documentation reproduced from package bamlss, version 1.1-2, License: GPL-2 | GPL-3*