Abstract #300800

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JSM 2003 Abstract #300800
Activity Number: 173
Type: Contributed
Date/Time: Monday, August 4, 2003 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #300800
Title: Bayesian Variable Selection in Proportional Hazard Model for Application to DNA Microarray Data
Author(s): Kyeong Eun Lee*+ and Bani K. Mallick
Companies: Texas A&M University and Texas A&M University
Address: Department of Statistics, College Station, TX, 77843-3143,
Keywords: Bayesian variable selection ; proportional hazard model ; microarray
Abstract:

When patient survival times are available with gene expression, it is of interest to select a significant set of genes associated with the survival time and to predict the survival time using these genes. Owing to small sample size and the large number of variables (genes), the selection process can be unstable. We propose a hierarchical Bayesian model for gene(variable) selection in Cox's proportional hazard model. We overcame the computation difficulty by including a random residual component in the linear predictor.This method is applied to a diffuse large B-cell lymphoma(DLBCL) data set (Alizadeh et al., 2000) to find a set of significant genes and estimate the survival function.

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