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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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A model selection criterion for clustered survival analysis with informative cluster size.

Li-Chu Chien1, Li-Ying Chang2, Chung-Wei Shen2

  • 1Center for Fundamental Science, Kaohsiung Medical University, Kaohsiung, Taiwan, ROC.

Pharmaceutical Statistics
|September 2, 2022
PubMed
Summary

We developed a new model selection criterion, Resampling Cluster Survival Information Criterion (RCSIC), for correlated survival data. RCSIC improves variable selection power for clustered survival analysis, even with informative cluster sizes.

Keywords:
correlated datainformative cluster sizessurvival datavariable selectionwithin-cluster resampling

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Correlated survival data presents challenges in model selection.
  • Cluster size can be informative, complicating standard approaches.
  • Existing methods may not adequately address variability in clustered data.

Purpose of the Study:

  • To introduce a novel model selection criterion for correlated survival data.
  • To account for informative cluster sizes and within-cluster variability.
  • To enhance variable selection power in clustered survival analysis.

Main Methods:

  • Proposed the Resampling Cluster Survival Information Criterion (RCSIC).
  • Utilized a Cox proportional hazards model weighted by inverse cluster size.
  • Incorporated within-cluster resampling and penalization for subsampling variability.

Main Results:

  • Simulations demonstrated satisfactory performance of RCSIC.
  • RCSIC showed robust variable selection power in clustered survival analysis.
  • The method is effective regardless of cluster size informativeness.

Conclusions:

  • RCSIC offers a robust approach to model selection for correlated survival data.
  • The criterion effectively handles informative cluster sizes and subsampling variability.
  • Applied to periodontal disease, RCSIC identified key risk factors for tooth loss.