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Variable selection via penalized generalized estimating equations for a marginal survival model.

Yi Niu1, Xiaoguang Wang1, Hui Cao1

  • 1School of Mathematical Sciences, Dalian University of Technology, Dalian, Liaoning, China.

Statistical Methods in Medical Research
|January 30, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new penalized generalized estimating equation method for variable selection in marginal survival models. The approach effectively handles clustered survival data and identifies key predictors for health research.

Keywords:
Clustered failure timecorrelation structurediverging number of predictorsgeneralized estimating equationsmarginal Cox’s proportional hazards modelmultivariate survival time

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Area of Science:

  • Biostatistics
  • Health Research Methodology
  • Survival Analysis

Background:

  • Clustered and multivariate survival times are common in biomedical research.
  • Marginal survival models are frequently employed for such data.
  • Variable selection is crucial when numerous predictors are present in survival modeling.

Purpose of the Study:

  • To propose a penalized generalized estimating equation (GEE) approach for variable selection and coefficient estimation in marginal survival models.
  • To address the challenge of high-dimensional predictors in clustered survival data analysis.
  • To simultaneously select important variables and estimate regression coefficients within a Cox proportional hazards framework.

Main Methods:

  • Utilized a penalized generalized estimating equation (GEE) approach under a sparsity assumption.
  • Employed a Cox's proportional hazards model for the marginal survival model.
  • Incorporated a prespecified working correlation matrix to model intra-cluster correlation.

Main Results:

  • Established asymptotic properties for the estimators derived from the penalized GEE.
  • Demonstrated that the number of candidate covariates can increase proportionally to the number of clusters.
  • Evaluated the method's performance via simulation studies and real-world data analysis.

Conclusions:

  • The proposed penalized GEE method is effective for variable selection in marginal survival models with clustered data.
  • The method provides a robust framework for analyzing high-dimensional clustered survival data in health research.
  • The approach facilitates simultaneous variable selection and coefficient estimation, enhancing model interpretability and accuracy.