# Is it possible to predict survival probability of the future (unseen) time points (in R)?

I have a time dependent cox model from 2018 to 2020. Running the cox model, I can look at survival probabilities from time0(month 0) to time24(month24) for each individual observation. Great!

Now, I actually want to get the survival probabilities up to time60 (5 years out). Does survival analysis allow me to predict that far out, or do I actually need to train my data on observations that last 5 years?

I'm using the survival package

data2<- tmerge(data, data, id=Policy_Number, tstart=0, tstop=24)

fit.data2<- coxph(object ~ var1+var2+var3+var4, data = data2)

fitresults <- survfit(fit.data2, newdata=data2)

Initially, I thought I can just replace my tstop to 60, but since my original data has tstops only up to 24, it will error out

You could consider a fully parametric model like a Weibull model, with functions like survreg() in R. In principle those can be extrapolated, as you have a complete function of time estimated from your data. In practice it's probably not a great idea, particularly for extrapolating from a final time of 2 years out to a time of 5 years. A lot of unexpected things can happen over 3 years.