Σεμινάριο Fotios S. Milienos

Ημερομηνία Εκδήλωσης: 
Παρασκευή, Δεκέμβριος 4, 2020 - 13:30

ΚΥΚΛΟΣ ΣΕΜΙΝΑΡΙΩΝ ΣΤΑΤΙΣΤΙΚΗΣ ΔΕΚΕΜΒΡΙΟΣ 2020

Fotios S. Milienos (Assistant Professor, Department of Sociology, Panteion University of Social and Political Sciences, Greece)

General Families of Cure Rate Models and Some of its Properties

Σύνδεσμος Google Meeting: meet.google.com/ikv-tkdp-hko

ΠΕΡΙΛΗΨΗ

In a great number of medical, social, educational, biological, and industrial applications, among others, there exist a non-negligible proportion of individuals/items which are not subject to the event (or recurrence of the event) of interest. These types of populations are typically studied by the theory of cure rate models (long term survival models) which, nowadays, play a substantial role in survival analysis (see, for example, the monographs by Maller and Zhou, 1996, and Ibrahim et al., 2005). Cure rate models offer efficient tools for modeling and estimating both the proportion of such individuals/items, usually termed as long-term survivors or immune or cured, and the survival times of the non-cured group. The most studied cure rate models can be defined through a competing cause scenario, where the random variables corresponding to the time-to-event due to each competing cause are independent and identically distributed, while the total number of competing causes is an unobservable discrete random variable. The literature consists of parametric and non/semi-parametric approaches, while the existence of right censored data, in the great majority of applications, makes the EM algorithm a popular option of estimating model parameters. In this talk, after a small introduction to the theory of cure rate models, we discuss a re-parameterization and some extensions of recently introduced families of cure rate models; the new models have as special cases, among others, the binary, the promotion time and the negative binomial cure rate model. Some of the properties of the proposed models and the problem of the estimation of model parameters are also discussed.