Jeff Xu wrote:
I am confused when trying the function survfit.
my question is: what does the survival curve given by plot.survfit mean?
is it the survival curve with different covariates at different points?
or just the baseline survival curve?
for example, I run the following code and get the survival curve
####
library(survival)
fit<-coxph(Surv(futime,fustat)~resid.ds+rx+ecog.ps,data=ovarian)
plot(survfit(fit,type="breslow"))
summary(survfit(fit,type="breslow"))
####
for the first two failure points, we have s(59|x1)=0.971, s(115|x2)=0.942
how can we guarantee that s(59|x1) is always greater than s(115|x2)?
since s(59|x1)=s_0(59)^exp(\beta'x1) and s(115|x2)=s_0(115)^exp(\beta'x2),
we can manipulate covariates to make s(59|x1) < s(115|x2), right?
do I miss anything?
In advance: I´m a beginner in survival analysis, too. But I think I can
help you with this.
plot(survfit(fit)) should plot the survival-function for x=0 or
equivalently beta'=0. This curve is independent of any covariates.
If you want to see the impact of residual-status=2 you could add
something like:
attach(ovarian)
ovarian_new <- data.frame(resid.ds=2,
rx=(mean(rx)),ecog.ps=mean(ecog.ps))
detach()
plot(survfit(fit), newdata=ovarian_new)
This should give you the survival-function for an average patient with
residual-status 2.
Regards
Bernhard
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