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Comparing k cumulative incidence functions through resampling methods.

Kam C Yuen1, Lixing Zhu, Dixin Zhang

  • 1Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong. kcyuen@hku.hk

Lifetime Data Analysis
|December 11, 2002
PubMed
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New statistical tests compare cumulative incidence functions in competing risks models. These tests, using resampling methods like bootstrapping, are effective for analyzing multiple risks simultaneously, even with moderate sample sizes.

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Competing risks models are essential for analyzing time-to-event data where multiple distinct events can occur.
  • Evaluating the equality of cumulative incidence functions (CIFs) is crucial for understanding disease progression and treatment effects.
  • Existing methods for comparing CIFs often have complex asymptotic distributions, necessitating robust approximation techniques.

Purpose of the Study:

  • To propose novel statistical tests for assessing the equality of k cumulative incidence functions within a competing risks framework.
  • To develop methods that are applicable under random censorship and do not require assumptions on the dependence structure between risks.
  • To investigate tests for ordered alternatives in competing risks scenarios.

Main Methods:

Related Experiment Videos

  • Development of test statistics based on processes related to cumulative incidence functions.
  • Utilization of the bootstrap method and random symmetrization for approximating critical values due to complex asymptotic distributions.
  • Application to scenarios involving k risks (k >= 2) under random censorship.

Main Results:

  • The proposed tests effectively compare multiple cumulative incidence functions simultaneously.
  • Simulation studies demonstrate good performance of the tests with moderate sample sizes.
  • The methodology is validated through a real-world application to cancer mortality data.

Conclusions:

  • The introduced tests provide a flexible and powerful tool for comparing cumulative incidence functions in competing risks models.
  • Resampling techniques offer a practical solution for determining significance in the presence of complex distributional properties.
  • The methods are suitable for diverse applications, including epidemiological studies of disease mortality.