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Copula-based analysis of dependent current status data with semiparametric linear transformation model.

Huazhen Yu1, Rui Zhang2, Lixin Zhang2,3

  • 1School of Mathematical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China. stayhz@zju.edu.cn.

Lifetime Data Analysis
|August 24, 2024
PubMed
Summary

This study introduces a new copula-based regression analysis for current status data with dependent censoring. The method enhances model identification and parameter estimation in epidemiological and survival analyses.

Keywords:
CopulaCurrent status dataDependent censoringIdentifiabilityNonparametric transformation

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Dependent censoring in current status data poses challenges for standard regression models.
  • Existing copula-based methods often struggle with model and parameter identification.
  • Accurate analysis is crucial in fields like epidemiology and tumorigenicity experiments.

Purpose of the Study:

  • To propose a novel copula-based regression analysis for current status data with dependent censoring.
  • To address limitations in model and parameter identification of existing methods.
  • To develop a flexible semiparametric approach where the association parameter is unspecified.

Main Methods:

  • Utilizing a general class of semiparametric linear transformation models.
  • Employing parametric copulas to model the dependence structure.
  • Implementing sieve maximum likelihood estimation with Bernstein polynomial approximation for nonparametric functions.

Main Results:

  • Demonstrated identifiability of the proposed semiparametric model under regularity conditions.
  • Established asymptotic consistency and normality of the developed estimators.
  • Validated the method's effectiveness through extensive simulations and a real data application.

Conclusions:

  • The proposed copula-based method offers a robust solution for analyzing current status data with dependent censoring.
  • The approach improves upon existing methods by addressing identifiability issues.
  • The technique is practically applicable and effective in real-world scenarios.