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Abstract Details
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Activity Number:
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62
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Type:
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Topic Contributed
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Date/Time:
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Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #305416 |
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Title:
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Bayesian Quantile Regression for Cystic Fibrosis Lung Function Data
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Author(s):
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Kameryn Denaro*+ and Barbara Bailey
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Companies:
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and San Diego State University
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Address:
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97 Costero Aisle, Irvine, CA, 92614, United States
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Keywords:
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longitudinal ;
Laplace distribution ;
FEV1
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Abstract:
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Cystic fibrosis (CF) is a genetic disease that dramatically decreases life expectancy and quality. The disease is characterized by polymicrobial infections which lead to lung remodeling and airway mucus plugging. CF has a unique challenge in diagnosing the severity of the disease. In 1955 the Cystic Fibrosis Foundation was founded in order to, "advance understanding of this little known disease, to create new treatments and specialized care for their children, and to find a cure". The average life span for people with CF who live to adulthood is approximately 37 years (U.S National Library of Medicine). There is a large push to have advancements in research for cystic fibrosis patients. In order to model the severity of the disease, Bayesian Quantile Regression are implented. We will compare the model's predicted severity with the severity index based on expert opinion.
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