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Bayesian penalized Buckley-James method for high dimensional bivariate censored regression models.

Wenjing Yin1, Sihai Dave Zhao1, Feng Liang2

  • 1Department of Statistics, University of Illinois, Urbana-Champaign, Champaign, IL, USA.

Lifetime Data Analysis
|March 3, 2022
PubMed
Summary

This study introduces a new method for variable selection in high-dimensional survival data, improving accuracy for disease progression and patient survival analysis. The approach effectively handles complex bivariate censored data in accelerated failure time models.

Keywords:
Bayesian penalizationBuckley-James estimatorMultivariate survival dataVariable selection

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Area of Science:

  • Biostatistics
  • Genomics
  • Survival Analysis

Background:

  • High-dimensional gene expression data analysis is crucial for understanding disease progression and patient survival.
  • Identifying key genes associated with disease outcomes remains a significant challenge in bioinformatics.

Purpose of the Study:

  • To develop a robust variable selection method for high-dimensional multivariate survival data.
  • To extend existing accelerated failure time (AFT) models to accommodate bivariate censored data.

Main Methods:

  • Proposes an estimation procedure for high-dimensional AFT models using bivariate censored data.
  • Extends the Buckley-James method by minimizing a penalized loss function with a bivariate spike-and-slab prior.
  • Imputes censored observations using the Kaplan-Meier estimator, avoiding parametric assumptions on error distributions.

Main Results:

  • The proposed method demonstrates superior performance over univariate survival data procedures for bivariate censored data.
  • Effectiveness is shown regardless of the correlation structure between the true events.
  • Provides a formal framework for handling bivariate survival data within AFT models.

Conclusions:

  • The novel method offers improved variable selection for high-dimensional gene expression data in survival analysis.
  • It provides a statistically sound approach for analyzing complex bivariate censored survival data.
  • The findings have implications for clinical trial analysis, as demonstrated in a myeloma study.