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Bayesian variable selection for parametric survival model with applications to cancer omics data.

Weiwei Duan1,2,3,4, Ruyang Zhang1,2,3,4, Yang Zhao1,2,3,4

  • 1Department of Biostatistics, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, Jiangsu, China.

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|November 8, 2018
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Summary

We developed Survival Expectation-Maximization Bayesian Variable Selection (SurvEMVS) for analyzing high-dimensional genetic data and time-to-event outcomes. This new model accurately identifies potential cancer survival biomarkers from large omics datasets.

Keywords:
Bayesian variable selectionEM algorithmNon-small cell lung cancerOmicsStomach adenocarcinomaSurvival analysis

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Area of Science:

  • Genomics
  • Biostatistics
  • Computational Biology

Background:

  • Simultaneous modeling of thousands of genetic markers is crucial for associating biomarkers with disease traits.
  • Existing expectation-maximization (EM) approaches to Bayesian variable selection (EMVS) are effective for continuous or binary outcomes.
  • Current EMVS methods are not suitable for time-to-event data analysis, limiting their use with public databases like The Cancer Genome Atlas (TCGA).

Purpose of the Study:

  • To extend the EMVS framework for high-dimensional parametric survival regression, enabling analysis of time-to-event outcomes.
  • To introduce a novel method, Survival EMVS (SurvEMVS), capable of handling large-scale omics data for survival analysis.

Main Methods:

  • Developed the Survival EMVS (SurvEMVS) model within a high-dimensional parametric survival regression framework.
  • Utilized a cyclic coordinate descent (CCD) algorithm for efficient iterations in the M-step.
  • Employed the extended Bayesian information criteria (EBIC) for optimal hyperparameter selection.

Main Results:

  • Evaluated SurvEMVS performance through numerical simulations and real-world data analysis.
  • Demonstrated the accuracy and computational efficiency of SurvEMVS in identifying potential survival-associated markers.
  • Identified potential biomarkers linked to lung and stomach cancer survival in TCGA datasets.

Conclusions:

  • The developed SurvEMVS model is effective for high-dimensional omics data analysis.
  • SurvEMVS successfully addresses the limitations of previous methods for time-to-event outcome analysis.
  • This approach facilitates the discovery of genetic markers influencing cancer survival.