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Bayesian Variable Selection under the Proportional Hazards Mixed-effects Model.

Kyeong Eun Lee1, Yongku Kim1, Ronghui Xu2

  • 1Department of Statistics, Kyungpook National University, Daegu, 702-701, Korea.

Computational Statistics & Data Analysis
|May 6, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a new variable selection method for correlated survival data using stochastic search variable selection (SSVS) within proportional hazards mixed-effects models (PHMM). This approach enhances model selection for complex survival data analysis.

Keywords:
MCMCcorrelated survival datamodel selectionmulti-center clinical trialproportional hazards mixed-effects modelstochastic search variable selection

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Survival Analysis

Background:

  • Developing models for correlated survival data is crucial, but model selection methods remain limited.
  • Existing stochastic search variable selection (SSVS) methods are primarily developed for normal mixture distributions.
  • Proportional hazards mixed-effects models (PHMM) offer a framework for correlated survival data but require robust variable selection.

Purpose of the Study:

  • To develop and evaluate a stochastic search variable selection (SSVS) approach for proportional hazards mixed-effects models (PHMM).
  • To address the limitations in model selection for correlated survival data.
  • To apply the developed method to a real-world clinical trial dataset with a debated variable selection history.

Main Methods:

  • Developed a novel SSVS approach tailored for PHMM, leveraging the inherent normal distribution of random effects.
  • Applied the SSVS method to the linear predictor within the Cox-type model framework.
  • Evaluated the performance of the SSVS-PHMM approach through simulation studies.

Main Results:

  • The SSVS approach efficiently searches the variable space within the PHMM framework.
  • The method demonstrated effectiveness in selecting relevant variables in simulated correlated survival data.
  • Successful application to a multi-center lung cancer clinical trial dataset, providing insights into a previously debated variable selection problem.

Conclusions:

  • The proposed SSVS approach provides an effective tool for variable selection in PHMM.
  • This method advances statistical modeling for correlated survival data.
  • The findings offer a robust solution for complex variable selection challenges in clinical trial analysis.