Modelagem de partição bayesiana para dados de sobrevivência de longa duração

Abstract

In this work we present a bayesian approach for the survival model with cure rate in the presence of covariates. In this perspective, the modelling is a direct extension of the long-term model of (Chen et al., 1999). This model is considered flexible in the sense that the effects of the covariates are measured locally using the bayesian partition model developed by Holmes et al. (1999). The bayesian partition model is a generic approach to problems of classification and regression where the space of covariates is divided in disjoint regions defined by a structure of tessellation. The extension to modelling local maintains the structure of the proportional hazards model that it is intrinsic of the long-term model(promotion time) (Rodrigues et al., 2009a). Application of this theory appears in several areas, for example in finance, biology, engineering, economics and medicine. We present a simulation study and apply the methodology to a set of data on the clinical studies.