Re: [R] Median expected survival
Hi Lars,
Graph it:
plot(pred_leuk)
will show that some of the survival curves do not reach 0.5 before you run out
of data. Thus the median is not estimated and you get NA.
Chris
-Original Message-
From: Lars Bishop [mailto:lars...@gmail.com]
Sent: Thursday, July 10, 2014 6:23 AM
To: r-help@r-project.org
Subject: [R] Median expected survival
Hi All,
Apologies for the simple question, but I could not find a straightforward
answer based on my limited knowledge of survival analysis.
I’m trying to obtain the predicted median survival time for each subject on
a new dataset from a fitted coxph{survival} or cph{rms} object. Would the
quantile.survfit function (as used below) return the expected median
survival? Why this function returns NAs in this case, when all predictors
have non-missing values?
As an alternative, I’ve tried to use the Quntile{rms} function as in my
second chunk of code, but in this case I get an error message (most likely
due to my lack of understanding as well).
library(MASS)
library(survival)
library(rms)
data(gehan)
leuk.cox <-coxph(Surv(time, cens) ~ treat + factor(pair), data = gehan)
leuk_new <- gehan[1:10, ] # take first 10 patients
pred_leuk <- survfit(leuk.cox, newdata=leuk_new)
quantile(pred_leuk, 0.5)$quantile
### alternative using rms
leuk.cox.rms <-cph(Surv(time, cens) ~ treat + factor(pair), data = gehan,
surv = T)
med <- Quantile(leuk.cox.rms)
Predict(leuk.cox.rms, data = leuk_new, fun=function(x)med(lp=x))
>Error in Predict(leuk.cox.rms, data = leuk_new, fun = function(x) med(lp =
x)) :
predictors(s) not in model: data
Thank you for your help.
Best,
Lars.
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Re: [R] Median expected survival
Hi, Lars. I don't understand well your question. why don't you simply type pred_leuk Call: survfit(formula = leuk.cox, newdata = leuk_new) records n.max n.start events median 0.95LCL 0.95UCL 1 4242 42 30 1 1 23 2 4242 42 30 7 5 NA 3 4242 42 30 15 10 NA 4 4242 42 30 NA NA NA 5 4242 42 30 8 5 NA 6 4242 42 30 23 22 NA 7 4242 42 30 8 6 NA 8 4242 42 30 NA 22 NA 9 4242 42 30 6 4 NA 10 4242 42 30 23 17 NA Yes, the quantile method returns the correct medians. The NAs usually appear when the unit belongs to a category for which median survival time has not yet been reached, so there is no way for the model to estimate the median time. You can see the expected survival curve with plot(predl_leuk) __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Median expected survival
Hi All,
Apologies for the simple question, but I could not find a straightforward
answer based on my limited knowledge of survival analysis.
Iâm trying to obtain the predicted median survival time for each subject on
a new dataset from a fitted coxph{survival} or cph{rms} object. Would the
quantile.survfit function (as used below) return the expected median
survival? Why this function returns NAs in this case, when all predictors
have non-missing values?
As an alternative, Iâve tried to use the Quntile{rms} function as in my
second chunk of code, but in this case I get an error message (most likely
due to my lack of understanding as well).
library(MASS)
library(survival)
library(rms)
data(gehan)
leuk.cox <-coxph(Surv(time, cens) ~ treat + factor(pair), data = gehan)
leuk_new <- gehan[1:10, ] # take first 10 patients
pred_leuk <- survfit(leuk.cox, newdata=leuk_new)
quantile(pred_leuk, 0.5)$quantile
### alternative using rms
leuk.cox.rms <-cph(Surv(time, cens) ~ treat + factor(pair), data = gehan,
surv = T)
med <- Quantile(leuk.cox.rms)
Predict(leuk.cox.rms, data = leuk_new, fun=function(x)med(lp=x))
>Error in Predict(leuk.cox.rms, data = leuk_new, fun = function(x) med(lp =
x)) :
predictors(s) not in model: data
Thank you for your help.
Best,
Lars.
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
