I have a question related to survival models: for instance, the survival probability of people that have a disease.
In all examples I've seen so far, the data used to estimate those models sometimes includes censored observations (people who are sick but have not died yet) but always includes non-censored observations (people who died and the life duration of whom we therefore know).
Now suppose you only have the sub-sample of censored observations (i.e people who are still alive, no one in the sample has died yet). I'm guessing a survival model will give biased estimates (lower than the actual survival), but that models like simple OLS are also biased... So I was wondering which model would be more appropriate for that type of data?
Thanks a lot!
Edit: There are averages for the whole population available online, for that variable (how long people stayed in their house). But at individual level, which is the data I want to use, that variable is not available, and all I have is when the individuals moved into their house (and so how long they stayed until now, but they might stay a few more years).
The question I want to answer is, given some individual characteristics, how to predict how long people stay in a house.
I hope this is clearer, thanks again!