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Abstract Details
Activity Number:
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78
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2011 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistical Computing
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Abstract - #303302 |
Title:
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Comparison Of Negative-Binomial And Extra-Poisson Models For Over-Dispersed Count Data: A Simulation-Based Study
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Author(s):
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Yaming Chen and Xiaochun Zhu*+ and John Connett
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Companies:
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Center for International Blood & Marrow Transplant Research and University of Minnesota at Twin Cities and University of Minnesota at Twin Cities
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Address:
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, , ,
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Keywords:
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Extra-Poisson ;
over-dispersion ;
count data ;
Negative-Binomial
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Abstract:
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Modeling count data is a hot topic in clinical trials and epidemiological studies. In reality, over-dispersion is often observed and the Negative-binomial (Neg-bin) model is often used in such situation. However, the performance of the Neg-bin model varies in existence of different underlying data distributions. Breslow (1984) proposed the Extra-Poisson (Ext-Poi) variation model, which has no pre-assumed distribution for the heterogeneity term. In this study, we simulated homogeneous Poisson processes under two distributions, one with the heterogeneity term following a normal distribution, and the other with the heterogeneity term following a gamma distribution. The simulated data under these two distributions were analyzed by both Neg-bin and Ext-Poi regressions. Based on our study, the Ext-Poi method is almost as powerful as Neg-bin model when the true distribution is Neg-bin. The Ext-Poi method yields appropriate Type I error rates for both situations. However, the Neg-Bin method can have excessively high Type I error rates when the data have Poisson-log-normal distribution. That is, the Ext-Poi method appears to be more robust than the Neg-bin method.
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