|
Activity Number:
|
548
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Thursday, August 6, 2009 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Section on Bayesian Statistical Science
|
| Abstract - #303538 |
|
Title:
|
A Bayesian Semiparametric Accelerated Failure Time Cure Model for Censored Data
|
|
Author(s):
|
Elizabeth C. Nelson*+ and Sujit Ghosh and Wenbin Lu
|
|
Companies:
|
North Carolina State University and North Carolina State University and North Carolina State University
|
|
Address:
|
Dept. of Statistics, Raleigh, NC, 27695-8203,
|
|
Keywords:
|
Long-term survival ; Markov chain Monte Carlo method ; Mixture density ; Posterior consistency
|
|
Abstract:
|
A semiparametric accelerated failure time cure model is developed for a population of subjects with a surviving fraction. The error distribution of the accelerated failure time component of the cure rate model is expressed as a nonparametric mixture of normal densities, thereby avoiding parametric assumptions. Markov chain Monte Carlo methods are used to generate samples from the posterior distribution of the regression coefficients to aid statistical inference. Posterior consistency is established under some regularity conditions providing large sample justification to the proposed model. Simulation studies validate the performance of the proposed model in finite samples and a real data set on breast cancer illustrates the method.
|
