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Activity Number:
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168
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Type:
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Contributed
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Date/Time:
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Monday, August 3, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #305464 |
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Title:
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Simulation Study of Hierarchical Modeling for Estimating Cancer 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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307 E 63rd St, 3rd Floor, New York, NY, 10021,
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Keywords:
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hierarchical models ; pseudo-likelihood ; genetic risk ; simulation
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Abstract:
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Many major genes have been identified that strongly influence the risk of cancer. It is thus critical to identify which genetic mutations confer an increased risk and which ones are harmless, so that one can appropriately counsel carriers of these mutations. This is a challenging task, since there can be numerous mutations in the gene and relatively little evidence available about each individual mutation. Capanu et al. (2008) employed hierarchical modeling using the pseudo-likelihood method to estimate the relative risks of individual rare variants and showed that one can draw strength from the aggregating power of hierarchical models to distinguish between variants that are harmful and those that are harmless. In this talk, we investigate the validity of this hierarchical modeling approach using simulations based on two real data sets from melanoma and breast cancer.
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