Survival analysis modeling with hidden censoring

There are well-established survival analysis methodologies for data sets that are complete, with accurate information on censoring. But what if they are not complete? In this article we consider how to analyze cases where “hidden censoring” occurs, where individuals have effectively left the study but the hospital is unaware of this. We develop a new Markov chain-based methodology for generating survival curves and hazard functions, and demonstrate this using a breast cancer data set from the Kurdistan region of Iraq