survival analysis simulation question

Hi Grace,

I seem to have sent an empty draft before. ?Anyway, something like
this might be an approach (untested):

require(rms)
set.seed(10)
dat <- data.frame(
?age = rnorm(500, 40, 10),
?race = factor(sample.int(2,500,TRUE), labels = c("a", "b")))
X <- model.matrix(~ age + race, data = dat)
b <- c(-2, .1, 3)
rates <- exp(X %*% b)
# distribution of survival time
dat$survtime ?<- 100 * rexp(500, rate = rates)
# indicator for censored/observed
dat$cens <- dat$survtime > 10
# new survival time values with censored
dat$survtime ?<- pmin(dat$survtime, 10)
test <- survreg(Surv(survtime, cens) ~ age + race, data = dat)
summary(test)

Cheers,

Josh

On Thu, May 10, 2012 at 7:41 PM, Grace Ma <grace.yanfei.m at gmail.com> wrote:

Hi,
I am trying to simulate a regression on survival data under a few
conditions:
1. Under different error distributions
2. Have the error term be dependent on the covariates

But I'm not sure how to specify either conditions. I am using the Design
package to perform the survival analysis using the survreg, bj, coxph
functions. ? Any help is greatly appreciated.

This is what I have so far:
survtime ?<- 10*rexp(500) ?#distribution of survival time
cens <- ifelse(survtime > 10, 0, 1) #indicator for censored/observed
survtime ?<- pmin(survtime, 10) #new survival time values with censored
info
age <- rnorm(200, 40, 10) ?#age variable
race <- factor(sample(c('a','b'),500,TRUE)) ?#categorical variable
test <- bj(Surv(survtime, cens) ~ rcs(age,5) + race)

? ? ? ?[[alternative HTML version deleted]]

--
Joshua Wiley
Ph.D. Student, Health Psychology
Programmer Analyst II, Statistical Consulting Group
University of California, Los Angeles
https://joshuawiley.com/