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Related Experiment Video

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Competing risks data analysis with high-dimensional covariates: an application in bladder cancer.

Leili Tapak1, Massoud Saidijam2, Majid Sadeghifar3

  • 1Department of Biostatistics and Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan 65175-4171, Iran.

Genomics, Proteomics & Bioinformatics
|April 25, 2015
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Summary

The elastic net method effectively selected significant genes for bladder cancer survival prediction, outperforming Lasso and boosting in high-dimensional data with competing risks.

Keywords:
Cause-specific hazardCompeting risksElastic netLassoMicroarraySubdistribution hazard

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Area of Science:

  • Bioinformatics
  • Genomics
  • Biostatistics

Background:

  • Microarray data analysis presents challenges due to high dimensionality and small sample sizes.
  • Existing variable selection methods often focus on single survival endpoints, neglecting competing risks.

Purpose of the Study:

  • To compare Lasso, elastic net, and likelihood-based boosting for variable selection in high-dimensional time-to-event data with competing risks.
  • To evaluate gene selection for bladder cancer survival prediction using these methods.

Main Methods:

  • Simulation study and analysis of a bladder cancer dataset.
  • Application of Lasso, elastic net, and likelihood-based boosting.
  • Evaluation using time-dependent receiver operator characteristic (ROC) curves and bootstrap prediction error curves.
  • Fine and Gray model fitting for significance testing.

Main Results:

  • Elastic net outperformed Lasso and boosting in variable selection for competing risks.
  • Elastic net identified 33 genes, with eight showing high significance (P<0.001) in the Fine and Gray model.
  • Selected genes improved predictive power beyond clinical variables alone.

Conclusions:

  • Elastic net is a superior method for predicting survival time in bladder cancer patients with high-dimensional competing risks data.
  • Microarray data provides valuable predictive information for bladder cancer survival.