Evolution of Bladder Cancer Estimated by Using a State-Space Model with a Semi-Markov Process and Censored Data: A Case Study
Résumé
We consider a Semi-Markov Process (SMP) to model the evolution of bladder cancer, which takes different states over time. A
multi-state model has been constructed and applied to data collected from 847 patients during a period of fifteen years. Biomedicine
databases usually contain censored data and this study shows that,
despite this, a good fit of the main survival measures is achieved by
using our specific model. This paper aims to present estimators for
the semi-Markov kernel, the survival function and the mean time
to disease progression. The strong consistency properties of the estimators are proved.
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