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Related Experiment Videos

Penalized likelihood in Cox regression

P J Verweij1, H C Van Houwelingen

  • 1Department of Medical Statistics, Leiden University, The Netherlands.

Statistics in Medicine
|December 15, 1994
PubMed
Summary
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This study improves Cox regression models by optimizing penalized likelihoods for stable coefficients. Methods were validated using ovarian cancer and kidney graft survival data, enhancing predictive accuracy.

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Medical Statistics

Background:

  • Instability in Cox regression coefficients can affect model reliability.
  • Penalized likelihood methods are used to stabilize regression coefficients.

Purpose of the Study:

  • To optimize the weight of the penalty function in penalized Cox regression.
  • To enhance the predictive value of survival models using cross-validation.

Main Methods:

  • Maximizing a penalized partial log-likelihood function.
  • Selecting optimal penalty weights by maximizing cross-validated partial log-likelihood.
  • Application to ovarian cancer and kidney graft survival datasets.

Main Results:

  • The proposed method effectively stabilizes regression coefficients in Cox models.

Related Experiment Videos

  • Optimal penalty weights improve model predictive performance.
  • Demonstrated utility in real-world survival data analysis.

    • Conclusions:

    • The approach provides a robust method for coefficient stabilization in Cox regression.
    • Maximizing cross-validated likelihood is an effective strategy for weight selection.
    • The findings have implications for survival analysis in clinical research.