Modelo destrutivo com variável terminal em experimentos quimiopreventivos de tumores em animais

The chemical induction of carcinogens in chemopreventive animal experiments is becoming increasingly frequent in biological research. The purpose of these biological experiments is to evaluate the effect of a particular treatment on the rate of tumors incidence in animals. In this work, the number o...

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Detalhes bibliográficos
Autor: Zavaleta, Katherine Elizabeth Coaguila
Formato: tesis de maestría
Estado:Versión publicada
Fecha de publicación:2012
País:Brasil
Recursos:Universidade Federal de São Carlos (UFSCAR)
Repositorio:Repositório Institucional da UFSCAR
Idioma:portugués
OAI Identifier:oai:repositorio.ufscar.br:20.500.14289/4561
Acesso em linha:https://repositorio.ufscar.br/handle/20.500.14289/4561
Access Level:acceso abierto
Palavra-chave:Estatística
Modelo destrutivo
Distribuição de Poisson
Distribuição binomial negativa
Modelos inflacionados de zeros
Modelo binomial negativa zero inflacionada
Experimentos quimiopreventivos
Destructive Model
Poisson Model
Negative Binomial Model
Zero-Inflated Poisson Model
Zero-Inflated Negative Binomial Model
Chemopreventive Experiments
CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA::ESTATISTICA
Descrição
Resumo:The chemical induction of carcinogens in chemopreventive animal experiments is becoming increasingly frequent in biological research. The purpose of these biological experiments is to evaluate the effect of a particular treatment on the rate of tumors incidence in animals. In this work, the number of promoted tumors per animal will be parametrically modeled following the suggestions given by Kokoska (1987) and Freedman et al. (1993). The study of these chemopreventive experiments will be presented in the context of the destructive model proposed by Rodrigues et al. (2010) with terminal variable that allows or censures the experiment at time of the animal death. Since the data analyzed in this field are subject to excess of zeros (Freedman et al. (1993)), we propose for the number of promoted tumors a negative binomial distribution (NB), a zero-inflated Poisson distribution (ZIP), and a zero-inflated Negative Binomial distribution (ZINB). The selection of these models will be made through the likelihood ratio test and the AIC, BIC criteria. The estimation of its parameters will be obtained by using the method of maximum likelihood, and further simulation studies will also be realized. As a future proposition to finalize this project, it is suggested the Bayesian methodology as an alternative to the method of maximum likelihood via the EM algorithm.