Modified zero-inflated negative binomial model with application to clinical trial in epileptic patients
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Date
2016-03-10
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Abstract
Marginalized models are in great demand by many researchers in the life sciences, particularly in clinical trials studies, epidemiology, health-economics, surveys and many others, since they allow generalization of inference to the entire population under study. For count data, standard procedures such as the Poisson regression and negative binomial model provide population average inference for model parameters. However, occurrence of excess zero counts and lack of independence in empirical data have necessitated their extension to accommodate these phenomena. These extensions, though useful, complicate interpretations of fixed effects. For example, the zero-inflated Poisson model accounts for the presence of excess zeros, but the parameter estimates do not have a direct marginal inferential ability as the base model, the Poisson model. Marginalizations due to the presence of excess zeros are underdeveloped though demand for them is interestingly high. The aim of this talk, therefore, is to present a developed marginalized model for zero-inflated univariate count outcome in the presence of overdispersion. Emphasis is placed on methodological development, efficient estimation of model parameters, and application to a clinical trial dataset. Results of simulation study performed to assess the performance of the model are also discussed.
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Seminar
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Marginalized models, clinical trials studies, epidemiology, population