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Abstract Details
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Activity Number:
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526
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #301949 |
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Title:
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Hierarchical CAR Models for Survival Data with Cure Rate Models
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Author(s):
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Yajun Liu*+
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Companies:
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University of Missouri
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Address:
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134 Middlebus Hall, Columbia, MO, 65211,
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Keywords:
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CAR ;
Cure Rate Model ;
Weibull ;
Survival ;
Ratio-of-Uniforms ;
Spatial
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Abstract:
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In this paper, we proposed a Bayesian hierarchical linear mixed model with spatial effects for right-censored survival data analysis. We compared a special case of the classic Cox model with Weibull hazard and the cure rate model. In both models, we applied the generalized linear mixed model with different effects to the hazard rates. A conditional autoregressive (CAR) prior is used to capture the spatial effects, which are decided by counties the survival subjects belong to. The computation is done by Gibbs Sampling. The Ratio-of-Uniforms method is used to sample from a non-standard conditional posterior density, and the ARM is also used to sample from log-concave densities. DICs are compared between different models. The simulation is done based on the Colon & Rectum Cancer (CRC) incidences in Iowa from SEER Data. The computation is implemented in Intel Fortran on Linux platform.
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Authors who are presenting talks have a * after their name.
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