Modelos de sobrevivência com fração de cura usando um termo de fragilidade e tempo de vida Weibull modificada generalizada

• Autor(a) principal: Calsavara, Vinicius Fernando
• Data de Publicação: 2011
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFSCAR
• Texto Completo: https://repositorio.ufscar.br/handle/ufscar/4546
• topic: Análise de sobrevivência; Fração de cura; Fragilidade; Distribuição Weibull; Modelos de longa duração; Modelo de mistura padrão; Modelo de fragilidade; Distribuição Weibull modificada generalizada; Long-term models; Standard mixture model; Frailty model; Generalized modified Weibull distribution; CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA::ESTATISTICA

In survival analysis, some studies are characterized by having a significant fraction of units that will never suffer the event of interest, even if accompanied by a long period of time. For the analysis of long-term data, we approach the standard mixture model by Berkson & Gage, where we assume the generalized modified Weibull distribution for the lifetime of individuals at risk. This model includes several classes of models as special cases, allowing its use to discriminate models. The standard mixture model implicitly assume that those individuals experiencing the event of interest possess homogeneous risk. Alternatively, we consider the standard mixture model with a frailty term in order to quantify the unobservable heterogeneity among individuals. This model is characterized by the inclusion of a unobservable random variable, which represents information that can not or have not been observed. We assume multiplicative frailty with a gamma distribution. For the lifetime of individuals at risk, we assume the Weibull distribution, obtaining the frailty Weibull standard mixture model. For both models, we realized simulation studies with the purpose of analyzing the frequentists properties of estimation procedures. Applications to real data set showed the applicability of the proposed models in which parameter estimates were determined using the approaches of maximum likelihood and Bayesian.