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Activity Number:
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357
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Type:
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Invited
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Date/Time:
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Wednesday, August 1, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #308019 |
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Title:
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The Use of Hierarchical Models for Estimating Relative Risks of Individual Genetic Variants
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Author(s):
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Marinela Capanu*+ and Colin B. Begg
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Companies:
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Memorial Sloan-Kettering Cancer Center and Memorial Sloan-Kettering Cancer Center
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Address:
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E 307 63rd St, 3rd Floor, New York, NY, 10021,
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Keywords:
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hierarchical models ; genetic risk ; Gibbs sampling ; pseudo-likelihood
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Abstract:
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Recent technological progress has led to rapid identification and sequencing of large numbers of genetic variants. Studying the associations between these variants and a particular disease is of great importance to epidemiologists in their quest to decipher the disease etiology. Hierarchical modeling is a technique which has been shown to provide more accurate and stable estimates of individual variants, by incorporating exchange of information through the higher levels of the multilevel model. This talk presents recent research on the application and implementation of hierarchical modeling regression to handle these issues using pseudo-likelihood and Gibbs sampling methods. A real data set from a melanoma case-control study is used to illustrate the methods.
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