On Jul 5, 2014, at 12:43 PM, Axel Urbiz wrote:
Thank you David. It is my understanding that using survfirsurvit
below I get the median predicted survival. I actually was looking
for the mean. I can't seem to find in the documentation how to get
that.
options(na.action=na.exclude) # retain NA in predictions
fit <- coxph(Surv(time, status) ~ age + ph.ecog, lung)
pred <- survfit(fit, newdata=lung)
head(pred)
There might be a way. I don't know it if so, so I would probably just
use the definition of the mean:
sum(summary(pred)$surv* summary(pred)$time)/sum( summary(pred)$time)
(I continue to take effort to keep my postings in plain text despite
my mail-clients's efforts to match your formatted postings. It adds to
the work of responders when you post formatted questions and responses.)
Thanks again,
Axel.
On Sat, Jul 5, 2014 at 1:54 PM, David Winsemius <dwinsem...@comcast.net
> wrote:
On Jul 5, 2014, at 5:28 AM, Axel Urbiz wrote:
Dear R users,
My apologies for the simple question, as I'm starting to learn the
concepts
behind the Cox PH model. I was just experimenting with the survival
and rms
packages for this.
I'm simply trying to obtain the expected survival time (as opposed
to the
probability of survival at a given time t).
What does "expected survival time" actually mean? Do you want the
median survival time?
I can't seem to find an option
from the "type" argument in the predict methods from coxph{survival}
or
cph{rms} that will give me expected survival times.
library(rms)
options(na.action=na.exclude) # retain NA in predictions
fit <- coxph(Surv(time, status) ~ age + ph.ecog, lung)
fit2 <- cph(Surv(time, status) ~ age + ph.ecog, lung)
head(predict(fit,type="lp"))
head(predict(fit2,type="lp"))
`predict` will return the results of the regression, i.e. the log-
hazard ratios for each term in the RHS of the formula. What you want
(as described in the Index for the survival package) is either
`survfit` or `survexp`.
require(survival)
help(pack=survival)
?survfit
?survexp
?summary.survfit
?quantile.survfit # to get the median
?print.summary.survfit
require(rms)
help(pack=rms)
The rms-package also adds a `survfit.cph` function but I have found
the `survest` function also provides useful added features, beyond
those offered by survfit
Thank you.
Regards,
Axel.
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David Winsemius, MD
Alameda, CA, USA
David Winsemius, MD
Alameda, CA, USA
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