Using classification models on censored data? I have a dataset in which I am trying to predict whether customers default on their loans or do not.
One of my variables is called DURATION which indicates the length of time before a person defaulted/did not default.
I understand that I can use survival trees and survival analysis to check how many months (in my case) a new loan applicant could go before defaulting. That is not my goal.
My goal is to predict whether one, new observation, would default or not.
My question:
Can I use binary logistic regression, for example, to predict that? Would I need to include the DURATION variable or would I need to drop it? If I keep it, do I need to use an existing method to place less weight on applicants who defaulted sooner rather than later?

The problem is that the nature of the default outcome is 3-fold: "Yes", "No", and "Not yet". The "Not yet" is the critical reason for requiring a method for censored data. A customer who is still paying off their loan is at risk of default, and each time point represents a different risk. No, a logistic model with adjustment for time does not a risk model make. To make matters worse, the "No" outcome - corresponding to customers who pay their loan in full - is a competing risk, or perhaps closure due to other reasons - transferred loan, sold loan, etc.
Once you fit an appropriate model for the competing risk of default and payment in full, you can use smoothed estimates of the conditional hazard functions to integrate over time and estimate the "lifetime" risk of either event. Of course, your data are probably limited, and the likelihood of having a "lifetime" observation is not feasible, so it would likely be better to speak of terms such as the 5 or 10 year absolute risk of default.
