Variable selection in the accelerated failure time model via the bridge method |
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Authors: | Jian Huang Shuangge Ma |
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Institution: | (1) Division of Entomology, University of Idaho, Moscow, ID 83844, USA;(2) Present address: Computer Science Department, University of Idaho, Moscow, ID 83844-1010, USA |
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Abstract: | In high throughput genomic studies, an important goal is to identify a small number of genomic markers that are associated
with development and progression of diseases. A representative example is microarray prognostic studies, where the goal is
to identify genes whose expressions are associated with disease free or overall survival. Because of the high dimensionality
of gene expression data, standard survival analysis techniques cannot be directly applied. In addition, among the thousands
of genes surveyed, only a subset are disease-associated. Gene selection is needed along with estimation. In this article,
we model the relationship between gene expressions and survival using the accelerated failure time (AFT) models. We use the
bridge penalization for regularized estimation and gene selection. An efficient iterative computational algorithm is proposed.
Tuning parameters are selected using V-fold cross validation. We use a resampling method to evaluate the prediction performance
of bridge estimator and the relative stability of identified genes. We show that the proposed bridge estimator is selection
consistent under appropriate conditions. Analysis of two lymphoma prognostic studies suggests that the bridge estimator can
identify a small number of genes and can have better prediction performance than the Lasso. |
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