Σεμινάριο Φώτης Μηλιένος
ΚΥΚΛΟΣ ΣΕΜΙΝΑΡΙΩΝ ΣΤΑΤΙΣΤΙΚΗΣ ΑΠΡΙΛΙΟΣ 2020
Φώτης Μηλιένος, Επίκουρος Καθηγητής, Τμήμα Κοινωνιολογίας, Πάντειο Πανεπιστήμιο
A General Family of Cure Rate Models and Some of its Properties
ΑΙΘΟΥΣΑ Τ105, ΤΡΟΙΑΣ 2, ΝΕΟ ΚΤΙΡΙΟ ΟΠΑ
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 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 also 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 way for estimating the parameters of the model. In this talk, after a small introduction to the theory of cure rate models, we discuss a re-parameterization of a recently introduced family of cure rate models; the new family has as special cases, among others, the binary, the promotion time and the negative binomial cure rate model. Some of the properties of the proposed model and the problem of the estimation of model parameters are also studied.