Prediction for Transition Probabilities in Multi-State Models

Abstract

The problem of censoring causes estimating transition probabilities in datasets with incomplete data to be interesting and complicated. The main focus of this thesis is on developing theory for a hazard based (Markov) multi-state model, a useful estimator of the transition probabilities and how to combine this with extra information available of an individual.