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Abstract Details
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Activity Number:
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86
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Type:
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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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Section on Statistics in Epidemiology
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| Abstract - #305828 |
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Title:
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Bayesian Approach of the Joinpoint Regression Model for Brain Cancer Data
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Author(s):
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Ram Kafle*+ and Netra Khanal and Chris P. Tsokos
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Companies:
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University of South Florida and University of Tampa and University of South Florida
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Address:
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14611 42nd Street, Tampa, FL, 33613, United States
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Keywords:
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joinpoint regression ;
Bayesian ;
Brain Cancer ;
SEER program ;
mortality ;
incidence
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Abstract:
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Joinpoint Regression is widely used to analyze cancer incidence and mortality data. In the present study we use the annual observed mortality or incidence counts that are probabilistically characterized by the Poisson distribution and model them using the log link function to observe the mortality or incidence trend over time. We proceed to develop a Bayesian model by considering the break points as random variable that are being characterize under various logical priors. In developing the subject methods we use the cancer mortality count from the Surveillance Epidemiology and End Results (SEER) data base.
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