You are mostly correct.
Because of the censoring issue, there is no good estimate of the mean survival
time. The survival curve either does not go to zero, or gets very noisy near
the right hand tail (large standard error); a smooth parametric estimate is
what
is really needed to deal with this.
For this reason the mean survival, though computed (but see the
survfit.print.mean option, help(print.survfit)) is not highly regarded. It is
not an option in predict.coxph.
Terry T.
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Hi,
if I got it right then the survival-time we expect for a subject is the
integral over the specific survival-function of the subject from 0 to t_max.
If I have a trained cox-model and want to make a prediction of the
survival-time for a new subject I could use
survfit(coxmodel, newdata=newSubject) to estimate a new
survival-function which I have to integrate thereafter.
Actually I thought predict(coxmodel, newSubject) would do this for me,
but I?m confused which type I have to declare. If I understand the
little pieces of documentation right then none of the available types is
exactly the predicted survival-time.
I think I have to use the mean survival-time of the baseline-function
times exp(the result of type linear predictor).
Am I right?
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