Statistical Models for Improving Prognosis of Heart Failure: Hazard Reconstruction, Clustering and Prediction of Disease Progression
Speaker: Francesca Ieva, University of Milano, Department of MathematicsAbstract:
Heart Failure (HF) is nowadays among the leading causes of repeated hospitalisations in over 65 patients. The longitudinal dataset resulting from the discharge papers and its analysis are consequently becoming of a great interest for clinicians and statisticians worldwide in order to have insights of the burden of such an extensive disease. We analysed HF data collected from the administrative databank of an Italian regional district (Lombardia), concentrating our study on the days elapsed from one admission to the next one for each patient in our dataset. The aim behind this project is to identify groups of patients, conjecturing that the variables in our study, the time segments between two consecutive hospitalisations, are Weibull differently distributed within each hidden cluster. Therefore, the comprehensive distribution for each variable is modeled by a Weibull Mixture. From this assumption we developed a survival analysis in order to estimate, through a proportional hazards model, the corresponding hazard function for the proposed model and to obtain jointly the desired clusters. We find that the selected dataset, a good representative of the complete population, can be categorized into three clusters, corresponding to “healthy”, “sick” and “terminally ill” patients. Furthermore, we attempt a reconstruction of the patient-specific hazard function, adding a frailty parameter to the considered model.
Friday, November 14th, 11:00 am –12:00 noon