prodlim (version 1.0.5)

SimSurv: Simulating survival data

Description

Censored event times are drawn from user specified conditional distributions given simulated covariates.

Usage

SimSurv(N,
	surv = list(dist="rweibull",
	  args=list(shape=1),
	  baseline=1,
	  link="exp",
	  coef=1,
	  transform=NULL,
	  method="simulation"),
	cens = list(dist="rexp",
	  args=NULL,
	  baseline=1/100,
	  link="exp",
	  max=NULL,
	  type="right",
	  coef=0,
	  transform=NULL,
	  method="transform"),
	cova = list(X1=list("rnorm",mean=0,sd=2),
	  X2=list("rbinom",size=1,prob=.5)),
	verbose=1,
	...)

Arguments

N

Sample size

surv

A list with the following arguments dist{ name of the function to draw the survival distribution} args{ list of extra arguments to `dist'} baseline{ the baseline risk} link{ names of the

cens

A list with the following elements dist{ name of the function to draw the censoring distribution} args{ list of extra arguments to `dist'} baseline{ the baseline risk} link{ names of the

cova

either a matrix with N rows, or a named list to generate the covariates. each entry is a list where the first element is the names of the function used to draw the covariate values and the remaining elements are arguments pass

verbose

Set to FALSE to shut up in simulations

...

used for convenient argument specification, e.g. surv.transform.X2=function(x)x^2 will overwrite a corresponding entry of surv for the transform of the covariate X2

Value

• A data.frame:
• time
  the right censored event times
• status
  the survival status
• X
  the values of the covariate X
• f.X
  the transformed values of the covariate X
• time
  the uncensored event times
• Attributes of the data.frame
• formula
• a formula to evaluate the generated event history object

Details

Possible distributions to generate covariates:

X.lognorm = list(dist = "rlnorm", meanlog = 2, sdlog = 0.4) X.unif = list(dist = "runif", min = 0, max = 10), X.exp = list(dist = "rexp", rate = 0.4) X.bernoulli = list(dist = "rbinom", size = 1, prob = 0.3) X.binom = list(dist = "rbinom", size = 4, prob = 0.8) X.nbinom = list(dist = "rnbinom", size = 3, mu = 2) X.poisson = list(dist = "rpois", lambda = 1.3)

References

Ralf Bender, Thomas Augustin, and Maria Blettner. Generating survival times to simulate Cox proportional hazards models by Ralf Bender, Thomas Augustin and Maria Blettner, Statistics in Medicine 2005; 24:1713-1723. Stat Med, 25(11):1978-9, 2006.

See Also

prodlim

Examples

SimSurv(10)

SurvData=SimSurv(100,cens.baseline=1/10,surv.baseline=2)

Hist(SurvData$time,SurvData$status)

prodlim(Hist(time,status)~1,data=SurvData)

plot(prodlim(Hist(time,status)~1,data=SurvData))

plot(SurvData,atrisk=FALSE,legend=FALSE)

SurvData=SimSurv(100,cens.baseline=1/10,surv.baseline=2,surv.coef=c(-1,-2),
         cova=list( X.exp = list(dist = "rexp", rate = 0.4),
         X.bernoulli = list(dist = "rbinom", size = 1, prob = 0.3)))
SurvData=SimSurv(100,cens.baseline=1/10,surv.baseline=2,surv.coef=c(-1,-2),cens.coef=c(0,1),
          cova=list( X.exp = list(dist = "rexp", rate = 0.4),
          X.bernoulli = list(dist = "rbinom", size = 1, prob = 0.3)))

coxph(Surv(time,status==0)~X.exp+X.bernoulli,data=SurvData)

coxph(Surv(time,status)~X.exp+X.bernoulli,data=SurvData)