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Activity Number:
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202
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #308184 |
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Title:
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A New Latent Cure Rate Marker Model for Survival Data
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Author(s):
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Sung Duk Kim*+ and Yingmei Xi and Ming-Hui Chen
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Companies:
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University of Connecticut and University of Connecticut and University of Connecticut
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Address:
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Department of statistics, Storrs, CT, 06269,
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Keywords:
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Classification ; Cure rates markers ; Latent variables ; MCMC ; Posterior distribution
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Abstract:
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We propose a new mixture model via latent cure rate markers for survival data with a cure fraction. In the proposed model, the latent cure rate markers are modeled via a multinomial logistic regression. The proposed model assumes that the patients may be classified into several risk groups based on their cure fractions. Based on the nature of the proposed model, a posterior predictive algorithm is also developed to classify patients into different risk groups. The proposed model not only bears more biological meaning, but also fits the data much better than several existing cure rate models based on the popular LPML measure. In addition, we develop necessary theories of the proposed models and efficient MCMC algorithms for carrying out Bayesian computation. A real data set from a prostate cancer clinical trial is analyzed in detail to further demonstrate the proposed methodology.
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