genSurvData: Simulate Survival Outcome Given Marker Values
In greyzoneSurv: Fit a Grey-Zone Model with Survival Data

Description Usage Arguments Details Value See Also Examples

This function simulates survival outcome with a fixed censoring rate based on a Weibull distribution given input values such as study recruitment period, patient marker values, their true risk groupings (1=high risk and 0=low risk), and true regression coefficients.

1 2	genSurvData(n, recruitment.yrs=2, baseline.hazard=365.25*5, shape=1, censoring.rate=0, beta.continuous, beta.binary=0, x, xhigh, ran.seed)

`n`	Sample size
`recruitment.yrs`	Patient recruitment period in years (default=2)
`baseline.hazard`	Baseline hazard, which is the mean survival time (in days) when covariates=0 (default=365.25*5 days)
`shape`	The shape parameter for the Weibull distribution (it is exponential when shape=1)
`censoring.rate`	Censoring rate
`beta.continuous`	A true regression coefficient that links the continuous marker values to the survival outcome
`beta.binary`	A true regression coefficient that links the high risk group to the survival outcome
`x`	A nx1 vector for the marker values
`xhigh`	A nx1 vector of 1s and 0s indicating patient true risk identities (1=high risk and 0=low risk)
`ran.seed`	Seed number for random number generation

The function can be used to generate survival data if you do not have any to try the grey-zone model.

It returns a list with two components: the simulated survival data in days and the final censoring rate (which should be the same as the input censoring rate).

em.func, cov.func, greyzone.func

## Generate package data called "mydata"
## Simulate high/low risk groupings, continuous marker values for each group, and survival data 
## so that the higher maker values correspond to shorter survival. 
n=300
censoring.rate=0.3
rate.lrisk=0.7   #rate of low risk
n.lrisk=n*rate.lrisk
n.hrisk=n-n.lrisk
mu=3  
beta.continuous=0.5
beta.binary=0.5
ran.seed=1000
set.seed(ran.seed)
x0=rnorm(n.lrisk, 0, 1)   #low risk patients have marker values distributed as Normal(0,1)
set.seed(ran.seed)
x1=rnorm(n.hrisk, mu, 1)   #high risk patients have maker values distributed as Normal(mu,1)
score=c(x0, x1)
score.high=c(rep(0, n.lrisk), rep(1, n.hrisk))
mydata=genSurvData(n=n, censoring.rate=censoring.rate,
                beta.continuous=beta.continuous, beta.binary=beta.binary, 
                x=score, xhigh=score.high, ran.seed=ran.seed)$data
dim(mydata)
head(mydata)