|
Activity Number:
|
374
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Tuesday, August 4, 2009 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
Section on Bayesian Statistical Science
|
| Abstract - #304794 |
|
Title:
|
Bayesian Flexible Joint Modeling of Survival and Curve Predictors
|
|
Author(s):
|
Xiaohui Wang*+ and Veera Baladandayuthapani and Bani K. Mallick and Kim-Anh Do
|
|
Companies:
|
The University of Texas Pan American and The University of Texas M.D. Anderson Cancer Center and Texas A&M University and The University of Texas M.D. Anderson Cancer Center
|
|
Address:
|
1201 West. Univ. Dr., Edinburg, TX, 78539,
|
|
Keywords:
|
Functional data analysis ; MCMC ; Mixed models ; Proportional hazards model ; Smoothing ; Survival.
|
|
Abstract:
|
We propose a model where the simple linear regression model has been modified by more flexible nonparametric spline-based approach, wherein the usage of the splines simplifies the parameterizations and the joint modeling framework allows synergistic benefit between the regression of functional predictors and proportional hazards modeling of survival data. Meanwhile, we explicitly model the number and location of changepoints. In addition, we propose a novel auxiliary variable scheme for a fully Bayesian estimation of our model, which not only allows dimension reduction of the parameter space but also allows efficient sampling from the conditional distributions. We illustrate our approach on a recent prostate cancer clinical trial study.
|
