This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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406
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract - #307407 |
Title:
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The Role of Proxy Genes in Predictive Models: An Application to Early Detection of Prostate Cancer
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Author(s):
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Jay Magidson and William Oh and Robert Ross and Philip Kantoff and Karl Wassmann*+
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Companies:
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Statistical Innovations Inc. and Mount Sinai School of Medicine and Infinity Pharmaceuticals and Dana-Farber Cancer Institute and Source MDx
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Address:
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199 Wells Ave. , Newton Center, MA, 02459,
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Keywords:
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gene expression ;
correlated component regression ;
high dimensional data ;
variable selection ;
logistic regression ;
proxy gene
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Abstract:
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The most important predictors often do not predict the outcome variable directly, but enhance the effects of other predictors in the model. The most important predictor in a 9-gene model for early detection of prostate cancer is SP1, a proxy gene, whose mean expression does not differ between cancer and normal subjects. However, SP1 is predictive of the pre-cancer expression of genes involved in its cellular process interactions, including some whose expression changed with cancer onset (prime genes). By subtracting out the predicted pre-cancer expression from the prime genes, the prime gene effects transform into stronger effects associated with post-pre change in prime gene expression. We present new models and methods that identify proxy and associated prime genes for inclusion in the model, from a potentially large pool of gene candidates. These methods generalize to survival models.
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