# 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 package `CoxFlexBoost` implementation `rSurvtime()` and only slightly modify the code. ```r ## 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 . - `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. ## Returns 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 `cox_bamlss`, `opt_Cox`, `sam_Cox`, `bamlss` ## Examples ```r ## 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) ```

simSurv function