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Flexible parametric copula modeling approaches for clustered survival data.

Sookhee Kwon1, Il Do Ha1, Jia-Han Shih2

  • 1Department of Statistics, Pukyong National University, Busan, South Korea.

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|August 3, 2021
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Summary

This study introduces a flexible one-stage method for analyzing clustered survival data using Archimedean copula models. The new approach offers more efficient and consistent estimation compared to existing methods, improving survival data analysis.

Keywords:
Archimedean copulaM-splinecopula modelone-stage estimationtwo-stage estimation

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Survival Analysis

Background:

  • Copula-based survival regression models are standard for clustered multivariate survival data.
  • Archimedean copulas effectively model dependence structures.
  • Existing one-stage methods often rely on restrictive parametric assumptions for marginal distributions.

Purpose of the Study:

  • To propose a flexible parametric Archimedean copula modeling approach using a one-stage likelihood procedure.
  • To overcome the limitations of existing methods regarding parametric assumptions for marginal distributions.
  • To provide a more efficient and consistent estimation method for clustered multivariate survival data.

Main Methods:

  • Developed a flexible parametric Archimedean copula model.
  • Employed a one-stage likelihood procedure for estimation.
  • Modeled unknown marginal baseline hazards using cubic M-spline basis functions to avoid specific parametric forms.

Main Results:

  • The proposed one-stage estimation method yields a consistent estimator.
  • The new method demonstrates superior efficiency compared to existing one- and two-stage methods.
  • The approach was validated through simulations and applied to three clinical datasets.

Conclusions:

  • The proposed flexible parametric Archimedean copula model with a one-stage procedure offers an efficient and consistent approach for analyzing clustered multivariate survival data.
  • Utilizing cubic M-splines for marginal baseline hazards enhances model flexibility without compromising estimation efficiency.
  • The method provides a valuable tool for researchers in biostatistics and related fields, with an accessible R function provided.