This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 532
Type: Contributed
Date/Time: Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #308085
Title: Risk Stratification Using Survival and Microarray Gene Expression Data
Author(s): Pingping Qu*+ and John D. Shaughnessy Jr. and Michael LeBlanc and Jeff Haessler and Bart Barlogie and John Crowley
Companies: Cancer Research and Biostatistics (CRAB) and University of Arkansas for Medical Sciences and Fred Hutchinson Cancer Research Center and Cancer Research and Biostatistics (CRAB) and University of Arkansas for Medical Sciences and Cancer Research and Biostatistics (CRAB)
Address: 1730 Minor Ave Suite 1900, Seattle, WA, 98101, USA
Keywords: survival ; microarray ; risk stratification ; cross-validation ; bagging
Abstract:

Multiple myeloma is characterized by tremendous heterogeneity in survival following standard therapies. The characterization of individual patients into risk categories with microarray gene expression data can provide valuable therapeutic guidance to the physician treating the disease. The main questions of interest in such risk stratifications are 1) what genes are associated with survival, and 2) does a patient have a high or low risk disease prognosis. Here we propose a powerful gene scoring approach in conjunction with cross-validation and bagging procedures to effect such stratification. We then evaluate the performance of these methods in terms of feature selection, risk group prediction, and survival function prediction with simulated datasets. We conclude with compelling results derived from the application of these methods to a recent myeloma pharmacogenomics study.


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