This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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Activity Number:
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553
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Type:
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Invited
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Date/Time:
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Wednesday, August 4, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #308944 |
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Title:
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Bayesian Clustering for Failure-Time Data
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Author(s):
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Gary Rosner*+ and Peter Mueller and Fernando Quintana
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Companies:
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The Johns Hopkins University and MD Anderson Cancer Center and Pontificia Universidad Católica de Chile
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Address:
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550 N. Broadway, Baltimore, MD, 21205, United States
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Keywords:
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Bayesian nonparametric methods ;
Product partition models ;
Survival analysis
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Abstract:
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We discuss a model-based clustering algorithm that exploits available covariates. The motivating application concerns predicting event-free survival of patients in a breast cancer trial. We build on product partition models (PPM) by defining an extension of the PPM to include a regression on covariates. The cohesion function in the PPM includes a new factor that increases the probability that experimental units with similar covariates will be included in the same cluster. Posterior predictive inference in this model formalizes the desired prediction.
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