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An R-Based Landscape Validation of a Competing Risk Model
05:37

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Published on: September 16, 2022

Global Partial Likelihood for Nonparametric Proportional Hazards Models.

Kani Chen1, Shaojun Guo, Liuquan Sun

  • 1Department of Mathematics, HKUST, Kowloon, Hong Kong, China.

Journal of the American Statistical Association
|July 31, 2012
PubMed
Summary
This summary is machine-generated.

A novel global partial likelihood method estimates covariate effects in nonparametric proportional hazards models, offering a consistent and efficient alternative to local methods. This approach also extends to partially linear models, improving slope parameter estimation.

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04:57

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Published on: October 23, 2020

Area of Science:

  • Survival Analysis
  • Nonparametric Statistics
  • Biostatistics

Background:

  • Existing local partial likelihood methods have limitations in estimating covariate effects.
  • Nonparametric proportional hazards models are crucial for analyzing time-to-event data.
  • Accurate estimation of covariate effects is essential for understanding risk factors.

Purpose of the Study:

  • To propose a global partial likelihood method as an alternative to local methods.
  • To estimate covariate effects in nonparametric proportional hazards models.
  • To evaluate the consistency, efficiency, and computational aspects of the new method.

Main Methods:

  • Developed a global partial likelihood estimator for covariate effects in nonparametric proportional hazards models.
  • Proved the estimator's consistency and semiparametric efficiency for linear functionals.
  • Demonstrated efficient Breslow-type estimation of the cumulative baseline hazard function.
  • Derived asymptotic bias and variance under regularity conditions.
  • Employed an iterative algorithm for computation.

Main Results:

  • The proposed global estimator is consistent and semiparametrically efficient.
  • Breslow-type estimation of the cumulative baseline hazard is efficient.
  • The method is computationally feasible via an iterative algorithm.
  • Simulations support the theoretical findings.
  • The approach is successfully extended to partially linear models.

Conclusions:

  • The global partial likelihood method provides a robust and efficient alternative for covariate effect estimation.
  • The method demonstrates strong theoretical properties and practical applicability.
  • The extension to partially linear models enhances its utility in complex survival data analysis.