I might just extend Thomas' succinct reply with a pointer to ?
survfit.coxph in the 'survival' package, which will provide the
baseline survival estimates at the mean values of the covariates from
the Cox model. It also has nice summary and plot methods and allows
for estimating predicted survival for given covariate values.
Using the 'ovarian' dataset:
fit <- coxph(Surv(futime, fustat) ~ age, data = ovarian)
# Note the default 'centered = TRUE' for basehaz()
> exp(-basehaz(fit)$hazard)
[1] 0.9880206 0.9738738 0.9545899 0.9334790 0.8973620 0.8624781
[7] 0.8243117 0.7750981 0.7244924 0.6734146 0.5962187 0.5204807
> summary(survfit(fit))$surv
[1] 0.9880206 0.9738738 0.9545899 0.9334790 0.8973620 0.8624781
[7] 0.8243117 0.7750981 0.7244924 0.6734146 0.5962187 0.5204807
You can also use:
summary(survfit(fit))
plot(survfit(fit))
...
You might also be interested in John Fox' web appendix on Cox models:
http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-cox-regression.pdf
HTH,
Marc Schwartz
On Dec 16, 2009, at 11:34 AM, tlum...@u.washington.edu wrote:
survival = exp(-hazard)
-thomas
On Wed, 16 Dec 2009, Laura Bonnett wrote:
Dear R-help,
I am trying to obtain the baseline survival estimate of a fitted
Cox model
(S_0 (t)). I know that previous posts have said use 'basehaz' but
this
gives the baseline hazard function and not the baseline survival
estimate.
Is there a way to obtain the baseline survival estimate or do I
have to use
the formula which does something like S(t) = exp[- the integral
from 0 to t
of h(u) du]?
Thank you for your assistance,
Laura
fit1 <-
coxph(Surv(tsecond/
365
,seccens
)~
stroke
(smess1
)
+
othnd
(smess1
)
+
relat
(smess1)+asleep(smess1)+abeeg1(smess1)+treat(smess1),data=smess1)
basehaz(fit1)
where stroke is a function which creates a binary variable from the
dataset
smess1 etc.
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