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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

Estimating and testing for center effects in competing risks.

Sandrine Katsahian1, Christian Boudreau

  • 1Département de Biostatistique et Informatique Médicale, Hôpital Saint-Louis, U717 INSERM, Paris, France. sandrine.katsahian@paris7.jussieu.fr

Statistics in Medicine
|February 23, 2011
PubMed
Summary
This summary is machine-generated.

This study presents a new penalized partial likelihood method for analyzing competing risks data with Gaussian frailty proportional hazards models. It also offers guidelines for choosing the best statistical test to detect center effects in such analyses.

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Competing Risks

Background:

  • Fine and Gray proposed a proportional hazards model for subdistribution to assess covariate effects on marginal failure probabilities in competing risks data.
  • Katsahian et al. extended this model to clustered time-to-event data by incorporating random center effects (frailties) into the subdistribution hazard.

Purpose of the Study:

  • To introduce an alternative estimation procedure for Gaussian frailties proportional hazards models in competing risks data with clustered effects.
  • To provide and compare four hypothesis tests for detecting center effects using Monte-Carlo simulations.
  • To offer practical guidelines for selecting the most appropriate test for center effects.

Main Methods:

  • An alternate estimation method based on penalized partial likelihood is introduced, compatible with standard survival analysis software.
  • Four distinct hypothesis tests for center effects are developed and evaluated through Monte-Carlo simulations.
  • The methodology is illustrated using registry data from bone marrow transplantation for acute myeloid leukemia (AML).

Main Results:

  • The proposed penalized partial likelihood approach offers a viable alternative for fitting Gaussian frailty models in competing risks settings.
  • Simulation results provide insights into the performance of the four hypothesis tests under various scenarios.
  • Statistical and numerical considerations lead to pragmatic recommendations for test selection.

Conclusions:

  • The study provides a novel and practical approach to analyzing clustered competing risks data using Gaussian frailty models.
  • The developed hypothesis tests and guidelines aid researchers in effectively assessing the significance of center effects.
  • The application to bone marrow transplantation data demonstrates the clinical relevance of the proposed methods.