How to get predictions in terms of survival time from a Cox PH model? I want to develop a prediction model (Cox PH) for all-cause mortality in a dataset of  participants of whom (almost) all have died at the end of follow-up (e.g. 1-year).
Instead of predicting the absolute risk of dying at a certain timepoint, I would like to predict the survival time (in months) for each individual. 
Is it possible to obtain such predictions in R (from e.g. a coxph-object) and, if yes, how can I do that? 
Many thanks in advance!
 A: @statBeginner Yes it will. It requires two steps:
x <- survfit(cox.ph.model, newdata = dataset)
dataset$Results <- summary(x)$table[,"median"]

but I am not sure if median time to survival is accurate enough.
A: Although I agree with these point, median survival IS clinically useful.
You might be interested in our work (and others) looking at using the median as a basis for survival intervals - we think these are more useful.
https://academic.oup.com/annonc/article/25/10/2014/2801274
A: The Cox Proportional Hazards model doesn't model the underlying hazard, which is what you'd need to predict survival time like that - this is both the model's great strength and one of it's major drawbacks.
If you are particularly interested in obtaining estimates of the probability of survival at particular time points, I would point you towards parametric survival models (aka accelerated failure time models). These are implemented in the survival package for R, and will give you parametric survival time distributions, wherein you can simply plug in the time you are interested in and get back a survival probability.
