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Abstract Details
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Activity Number:
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589
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #302320 |
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Title:
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A Modeling Strategy for Future Survival Days Given a Follow-Up Doubly Censored Sample of Liver Cancer Patients
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Author(s):
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Vincy Samuel*+ and Hafiz M. R. Khan
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Companies:
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Florida International University and Florida International University
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Address:
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Department of Epidemiology & Biostatistics, Miami, FL, 33199, USA
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Keywords:
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Doubly censored sample ;
Asymmetrical model ;
Likelihood function ;
Highest predictive density interval ;
Bayesian method ;
Predictive inference
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Abstract:
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This talk will focus on a novel application of Bayesian method to estimate future survival days given a set of doubly censored survival days of liver cancer patients. The derivation of predictive densities for future survival days from an asymmetrical model with a Bayesian frame-work will be discussed. A real data example of hepatocellular carcinoma of liver cancer patients who were diagnosed at a local hospital is used to illustrate the predictive results. Furthermore, subgroup levels of the male and female patients' follow-up survival days are considered to obtain the future survival days. The mean, standard deviation, and 95% highest predictive density interval of the future survival days are obtained by making use of the Bayesian method.
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