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K-means for shared frailty models.

Usha Govindarajulu1, Sandeep Bedi2

  • 1Center for Biostatistics, Department of Population Health & Policy Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, NY, New York, USA. usha.govindarajulu@mountsinai.org.

BMC Medical Research Methodology
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Summary

This study introduces a k-means survival grouping algorithm for shared frailty models. The k-means approach effectively creates patient groups, proving as reliable as traditional methods for survival analysis.

Keywords:
HeterogeneityModified k-means algorithmShared frailtySurvival analysis

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Area of Science:

  • Biostatistics
  • Machine Learning in Healthcare
  • Survival Analysis

Background:

  • Unmeasured confounding in survival models can bias results.
  • Shared frailty models account for unobserved heterogeneity but require pre-defined groups.
  • Existing grouping variables may not fully capture complex patient stratifications.

Purpose of the Study:

  • To investigate the application of the k-means algorithm for group definition in survival analysis.
  • To integrate k-means-derived groups into shared frailty models for improved confounding control.
  • To evaluate the performance of k-means clustering against traditional grouping methods in survival data.

Main Methods:

  • Development of a novel k-means survival grouping algorithm.
  • Comparison of shared frailty models using k-means groups versus standard grouping variables.
  • Validation through simulations and analysis of a real-world dataset.

Main Results:

  • The k-means survival grouping algorithm demonstrated comparable performance to traditional frailty clustering.
  • Effectiveness was consistent across simulations and real data, irrespective of case rates or censoring levels.
  • K-means provides a reliable method for generating groups for shared frailty models.

Conclusions:

  • The proposed k-means algorithm is a trustworthy tool for creating patient groups in survival analysis.
  • It offers a viable alternative when no explicit grouping variable is available or for comparing existing groups.
  • This method enhances the ability of shared frailty models to capture unmeasured confounding.