Hello!
I have something like this:
test1 <- data.frame(intx=c(4,3,1,1,2,2,3),
status=c(1,1,1,0,1,1,0),
x1=c(0,2,1,1,1,0,0),
x2=c(1,1,0,0,2,2,0),
sex=c(0,0,0,0,1,1,1))
and I can easily fit a cox model:
library(survival)
coxph(Surv(intx,status) ~ x1 + x2 + strata(sex),test1)
However, I want to write my own function, fit the model inside this function
and then do some further computations.
f <- function(time, event, stratum, covar )
{
fit <- coxph(Surv(time,event) ~ covar[[1]] + covar[[2]] + strata(stratum))
fit
#... do some other stuff
}
attach(test1)
f(intx, status, sex, list(x1,x2))
This works fine when I have exactly two covariates. However, I would like to
have something that I can use with an arbitrary number of covariates. More
precisely, I need something more general than covar[[1]] + covar[[2]].
Any ideas?
Thanks,
Franco
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