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.
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