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On path restoration for censored outcomes.
Brent A Johnson1, Qi Long, Matthias Chung
1Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia 30322, USA. bajohn3@emory.edu
Biometrics
|April 5, 2011
Summary
This study introduces a new two-stage method for estimating regularized coefficients in survival data, addressing limitations in current techniques for censored outcomes. The novel approach successfully identifies key genes in lung adenocarcinoma, offering a promising alternative for variable selection in statistical modeling.
Area of Science:
- Statistical Science
- Biostatistics
- Genomics
Background:
- Dimension reduction, model selection, and variable selection are critical in statistical science.
- Existing regularization methods for penalized least squares and likelihood in survival data have limitations.
- Current strategies fail to estimate the entire regularized coefficient path for the Buckley-James estimating function with observed survival data.
Purpose of the Study:
- To propose a novel two-stage method for estimating and restoring the entire Dantzig-regularized coefficient path for censored outcomes.
- To address the failure of current strategies when estimating the full regularized coefficient path using the Buckley-James estimating function.
- To apply the proposed method to a real-world microarray study and evaluate its performance in simulation studies.
Main Methods:
- A novel two-stage method is proposed to estimate and restore the entire Dantzig-regularized coefficient path.
- The method is applied within a least-squares framework for censored outcomes.
- The approach is tested on a lung adenocarcinoma microarray dataset and through simulation studies.
Main Results:
- The proposed method successfully estimates and restores the entire regularized coefficient path for censored outcomes.
- Application to a lung adenocarcinoma dataset (n=200, p=1036) identified 10 consistently selected genes and 14 additional genes for investigation.
- Simulation studies indicate the proposed path restoration and variable selection technique performs comparably to methods using convex loss functions.
Conclusions:
- The novel two-stage method effectively handles censored outcomes for estimating regularized coefficient paths.
- The technique demonstrates utility in identifying relevant gene predictors in complex datasets like microarrays.
- The proposed approach offers a viable and potentially superior alternative to existing methods, particularly when dealing with Buckley-James estimating functions.
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