Modelos para dados grupados e sensurados aplicados à área biológica: comparação usando fator de Bayes

Detalhes bibliográficos
Ano de defesa: 2015
Autor(a) principal: Andrade, Sophia Lanza de [UNESP]
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Estadual Paulista (Unesp)
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://hdl.handle.net/11449/132053
http://www.athena.biblioteca.unesp.br/exlibris/bd/cathedra/04-11-2015/000851915.pdf
Resumo: Grouped data is a particular case of survival data with interval censoring, that occurs when the observations are evaluated at the same time intervals, and is generally associated at data with a large number of draws, or draws ratio more than 25% (Chalita et al., 2002). It can be analyzed considering discrete-time and tting models at the probability of an individual fails in a certain interval, given that survived the previous one (Lawless, 2002). Among the possible models adapted to this type of data we can mention the Logistic Model and the Cox's Model. The comparison between the t of these two models can be made using the score test proposed by Colosimo et al. (2000), nonparametric Bootstrap or the Akaike Information Criterion (AIC). An alternative to these techniques, from the Bayesian point of view, is the Bayes Factor. The purpose of this study is to compare the t of the Logistic Model with the Cox's Model to grouped and censoring data using, initially, classic model selection criteria: Akaike Information Criterion (AIC), Akaike Information Criterion corrected (AICc) and Bayesian Information Criterion (BIC). After that, was used the Bayes Factor, as well as Deviance Information Criterion (DIC) and adaptations of the classic model selection criteria above mentioned, using the posteriori sample generated by a MCMC method. As an example, was used a data set related to a clinical manifestation of Chagas disease known as chagasic megacolon (Almeida, 1996)