[R] survival::predict.coxph

[Terry Therneau]

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?