# [R] survival::survfit,plot.survfit

Bernhard Reinhardt bernhard.reinhardt at dlr.de
Thu Feb 26 10:03:24 CET 2009

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

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