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Updated: Jun 20, 2026

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

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Score tests for independence in semiparametric competing risks models.

Mériem Saïd1, Nadia Ghazzali, Louis-Paul Rivest

  • 1Département de mathématiques et de statistique, Université Laval, 1045 rue de médecine, Québec City, QC GIV OA6, Canada. Meriem.Said@mat.ulaval.ca

Lifetime Data Analysis
|August 29, 2009
PubMed
Summary
This summary is machine-generated.

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This study introduces independence score tests to validate the assumption of independent failure times in competing risks models. The proposed methods, using semiparametric proportional hazards regressions, offer a way to detect dependencies between risks.

Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Competing risks models often assume independent latent failure times for analytical simplicity.
  • This independence assumption allows the use of standard statistical tools for right-censored lifetime data.
  • However, violations of this assumption can lead to biased results in survival analysis.

Purpose of the Study:

  • To propose simple independence score tests for assessing the validity of the independence assumption in competing risks models.
  • To develop statistical tools for detecting dependency between competing risks when modeled using semiparametric proportional hazards regressions.
  • To provide a method for analyzing dependent competing risks data.

Main Methods:

  • Development of independence score tests based on semiparametric proportional hazards regression models.

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  • Derivation of score tests for alternatives involving copula-based dependency between risks.
  • Construction of test statistics by augmenting partial likelihoods with a dependency variable.
  • Utilizing stochastic integrals for variance estimation of the score function and Fisher information matrix.
  • Application of Pitman efficiencies for comparing the power of different test statistics.

    • Main Results:

    • The proposed score tests are shown to be effective in detecting dependencies between competing risks.
    • The methodology is illustrated through a simulation study and a numerical example.
    • The derived variance estimator provides a robust way to estimate test statistic variability.
    • The tests are sensitive to alternatives specified by copula models.

    Conclusions:

    • The developed independence score tests provide a valuable tool for validating the crucial independence assumption in competing risks analysis.
    • The methodology allows for the identification and analysis of dependent competing risks, improving the accuracy of survival predictions.
    • This work contributes to the advancement of statistical methods for complex survival data analysis.