L. Acosta, C. Armero
Multi-state models deal with the temporal evolution of individuals within a target population as they transition between various states, often representing different disease or health conditions in medical contexts. The outcomes of interest are transition times between states and the trajectories of individuals through them. Our work presents a Bayesian analysis of the different probabilities associated with a multi-state model, such as the transition probabilities between states and those associated with the different absorbing states.
We used retrospective data from a cohort study of hospitalized laboratory confirmed cases registered by the 14 hospitals included in the Influenza Surveillance System of Catalonia from 1 October 2017 to 22 May 2018. Patients initially admitted to the hospital were sent to the ICU or ward, and eventually could be discharged, referred to a long-term care facility, or die. Results provide a better insight of the clinical evolution of the influenza disease
Palabras clave: Multi-state models, Conditional probabilities, Confirmed influenza, Hospitalization.
Programado
Bioestadística
8 de noviembre de 2023 17:20
HC4: Sala Sacristía