I'm new to Survival Analysis. Usually in survival analysis, we want to model the survival function progress w.r.t time. This is normally done through Cox model, or KM-model within a specific time horizon
However, this assume that we know exactly when the hazard event occurred. If we want to model a system which we can only observed the state periodically, but not continuously. Then modeled using traditional cox model, this model will be biased as we don't know the exact time when the hazard event occurred
For example, we want to model a survival function on a system with multiple agents which can be either alive, or dead, but we can only observed at a specific time interval. Then we know the amount of alive, and dead agents at time step t-1, and t. But we can't model how the survival function had progress between that interval
So in general, how does this kind of observational noise be deal with such that the model is not biased?