  Due to the presence of multiple types of adverse events with different levels
of severity, the analysis of longitudinal toxicity data is a difficult task in
cancer studies. In this work, a novel approach based on latent Markov models
and compositional data techniques is proposed. The latent status of interest is
the Latent Overall Toxicity (LOTox) condition of each patient. The main
objectives consist in identifying different latent states of overall toxicity
burden and investigating the evolution of individual toxicity risk during
cancer treatment. This methodology is applied to osteosarcoma treatment data to
provide novel techniques that may support medical decisions in childhood cancer
therapy.
