Estimação clássica e Bayesiana em modelos de sobrevida com fração de cura

In Survival Analysis, long duration models allow for the estimation of the healing fraction, which represents a portion of the population immune to the event of interest. Here we address classical and Bayesian estimation based on mixture models and promotion time models, using different distributions (exponential, Weibull and Pareto) to model failure time. The database used to illustrate the implementations is described in Kersey et al. (1987) and it consists of a group of leukemia patients who underwent a certain type of transplant. The specific implementations used were numeric optimization by BFGS as implemented in R (base::optim), Laplace approximation (own implementation) and Gibbs sampling as implemented in Winbugs. We describe the main features of the models used, the estimation methods and the computational aspects. We also discuss how different prior information can affect the Bayesian estimates

Grantor dc:publisher

Universidade Federal do Rio Grande do Norte

Year dc:date.issued

2013

Author dc:creator

• Almeida, Josemir Ramos de

Advisor dc:contributor.advisor

• Andrade, Bernardo Borba de

• Análise de sobrevivência. Modelos de longa duração. Método de Laplace. MCMC
• Survival analysis. Models of long term. Method Laplace. MCMC

Statement dc:rights

• Acesso Aberto

Language dc:language

por

Repository record dc:identifier.uri

https://repositorio.ufrn.br/jspui/handle/123456789/17012

OAI identifier oai:identifier

oai:repositorio.ufrn.br:123456789/17012