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

Abstract Details

Activity Number: 148
Type: Invited
Date/Time: Monday, August 2, 2010 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract - #305916
Title: Feature Selection for Censored Outcomes with Simultaneous Adjustment for Nonlinear Covariate Effect and Improved Risk Scores of Prostate Cancer Recurrence
Author(s): Qi Long*+ and Brent Johnson and Carlos S. Moreno
Companies: Emory University and Emory University and Emory University
Address: 1518 Clifton Rd. NE, 3rd floor., Atlanta, GA, 30322,
Keywords: Accelerated Failure Time Model ; Feature Selection ; LASSO ; Partly Linear Model ; Penalized Splines ; Rank Estimation
Abstract:

In biomedical studies, it is of substantial interest to conduct feature selection and develop risk scores using high-dimensional data such as gene expression data, when the outcome of interest may be censored. In the presence of well-established risk factors such as clinical variables, investigators often prefer a feature selection procedure that also adjusts for these clinical variables. We propose to conduct feature selection using Lasso-type estimation in a novel partly linear accelerated failure time model, where the gene expression data are modeled parametrically and the clinical variables are modeled nonparametrically through penalized regression splines. We show through simulation studies that our method compares favorably with the methods that assume parametric covariate effects. We apply the proposed method to a recent prostate cancer study.


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