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Abstract Details
Activity Number:
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289
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #304180 |
Title:
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Bayesian Survival Analysis via Transform-both-sides Model
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Author(s):
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Jianchang Lin*+ and Debajyoti Sinha and Stuart Lipsitz and Adriano Polpo
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Companies:
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Millennium: The Takeda Oncology Company and Florida State University and Harvard Medical School and Federal University of Sao Carlos
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Address:
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35 Landsdowne St., Cambridge, MA, 02139,
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Keywords:
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Survival Analysis ;
Median regression ;
Dirichlet process ;
Transform-both-sides
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Abstract:
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We present a novel semiparametric survival model with log-linear median regression function. This wide class of models is an useful alternative to the popular Cox (1972) model and linear transformation models (Cheng et al., 1995). Compared to existing semiparametric models, our models have many important practical advantages, including interpretation of the regression parameters via the median and the ability to address heteroscedasticity. We demonstrate that our modeling techniques facilitate the ease of prior elicitation and computation for both parametric and semiparametric Bayesian analysis of survival data. We illustrate the advantages of our modeling, as well as model diagnostics, via reanalysis of a small-cell lung cancer study. Results of our simulation study provide further guidance regarding appropriate modeling in practice.
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