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Abstract Details

Activity Number: 289
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #304180
Title: Bayesian Survival Analysis via Transform-both-sides Model
Author(s): Jianchang Lin*+ and Debajyoti Sinha and Stuart Lipsitz and Adriano Polpo
Companies: Millennium: The Takeda Oncology Company and Florida State University and Harvard Medical School and Federal University of Sao Carlos
Address: 35 Landsdowne St., Cambridge, MA, 02139,
Keywords: Survival Analysis ; Median regression ; Dirichlet process ; Transform-both-sides

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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