JSM 2004 Online Program

Abstract #300277

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Activity Number:	258
Type:	Invited
Date/Time:	Tuesday, August 10, 2004 : 2:00 PM to 3:50 PM
Sponsor:	WNAR
Abstract - #300277
Title:	Bayesian Semiparametric Models for Survival Data with Time-dependent Covariates
Author(s):	Wesley O. Johnson*+ and Timothy E. Hanson
Companies:	University of California, Davis and University of New Mexico
Address:	369 Kerr Hall, Davis, CA, 95616,
Keywords:	time-dependent covariates ; mixture of Polya trees prior ; Markov chain Monte Carlo methods
Abstract:	We discuss three models for survival data with time-dependent covariates(TDC); one by Cox and Oakes (1984), the second by Cox (1972), and a new model for TDCs that parallels Cox's original construction, only for the Accelerated Failure Time Model (AFT). We develop a Markov chain Monte Carlo approach based on modeling the baseline survival distribution as a mixture of Polya Tree processes for each model. A substantive illustration is presented using Cerebral Edema data, with emphasis on the practical implementation and use of these models.

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