:: II Workshop – Bayesianismo: Fundamentos e Aplicações ::

Palestra 9 (sexta às 11h00)
The Generalized Negative Binomial Cure Rate Model For Survival Data In Presence Of Latent Competing Causes.
Francisco Louzada–Neto (DEs–UFSCAR).

Abstract:
In lifetime studies, the occurrence of an event might be caused by one, among many, competing causes. Also, both the number of causes and the time-to-event associated with each cause may be not observed. Adding to this situation the existence of a proportion of individuals which is not susceptible to the occurrence of the event of interesting, we have a scenario of competing causes with a cure fraction. In this paper, we propose a general survival model for accommodating data in the presence of latent competing causes and cure fraction. We assume the number of competing causes following a generalized negative binomial distribution. The advantage of this assumption is to incorporating in to the analysis characteristics of the treatment, such as the number of doses, the time interval between doses and the efficiency of each dose. The lifetimes related to each competing cause are assumed to follow a Weibull distribution. Parameter estimation of the proposed model is straightforward via Bayesian estimation procedure. A simulation study was carried out in order to verify the frequentist properties of the credible intervals for the parameter of interest. We illustrate the usefulness of our model by applying it to a real data on breast cancer. The work is co-authored by Juliana Cobre (PPG-Es-UFSCar)ICMC-USP) and Gleici Silva Castro Perdoná (DMS-FMRP-USP).

Promotion and Support:
			CAPES-Proex Pós–graduação em Estatística-USP