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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Subdistribution hazard models for competing risks in discrete time.

Moritz Berger1, Matthias Schmid1, Thomas Welchowski1

  • 1Department of Medical Biometry, Informatics and Epidemiology, Faculty of Medicine, University of Bonn, Sigmund-Freud-Str. 25, Bonn, Germany.

Biostatistics (Oxford, England)
|November 13, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new discrete-time model for competing risks analysis, adapting the Fine and Gray proportional subdistribution hazards model for situations with discrete event times. The method ensures consistent parameter estimation for longitudinal studies.

Keywords:
Competing risksDiscrete time-to-event dataRegression modelingSubdistribution hazardSurvival analysis

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Medical Statistics

Background:

  • The Fine and Gray proportional subdistribution hazards model is standard for competing risks in longitudinal studies.
  • This model is unsuitable for discrete time scales common in epidemiological follow-ups.
  • Exact event times are often unknown, necessitating discrete-time analysis.

Purpose of the Study:

  • To develop a discrete-time adaptation of the Fine and Gray model.
  • To enable competing risks analysis when event times are not continuously measured.
  • To provide a robust statistical method for discrete longitudinal data.

Main Methods:

  • Proposed a novel technique for modeling subdistribution hazards in discrete time.
  • Utilized a weighted Maximum Likelihood (ML) estimation scheme for binary regression.
  • Ensured estimators are consistent and asymptotically normal.

Main Results:

  • The developed method provides consistent and asymptotically normal estimators.
  • Successfully adapted the proportional subdistribution hazards model for discrete time scales.
  • Demonstrated the model's applicability through a real-world case study.

Conclusions:

  • The proposed discrete-time method effectively extends the Fine and Gray model.
  • This approach is valuable for analyzing competing risks in epidemiological and clinical studies with discrete time data.
  • The method offers a statistically sound alternative for discrete longitudinal competing risks analysis.