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
> 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
ovarian_new <- data.frame(resid.ds=2,
This should give you the survival-function for an average patient with
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