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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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On the analysis of discrete time competing risks data.

Minjung Lee1, Eric J Feuer2, Jason P Fine3,4

  • 1Department of Statistics, Kangwon National University, Chuncheon, Gangwon 24341, South Korea.

Biometrics
|April 18, 2018
PubMed
Summary

This study introduces new regression methods for discrete time competing risks data, improving analysis for cancer registries like SEER. The approach enhances predictions of cumulative incidence functions for better health outcomes research.

Keywords:
Cause-specific hazard functionCompeting risksCumulative incidence functionDiscrete timeMultiple time scalesTransformation regression model

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Continuous time regression methods are standard for competing risks data.
  • Discrete time event data, common in cancer registries (e.g., SEER), pose challenges for existing methods.
  • Naive application of continuous methods to discrete data yields inappropriate results.

Purpose of the Study:

  • To develop appropriate regression methodology for discrete time competing risks data.
  • To enable accurate estimation of cause-specific hazard functions and prediction of cumulative incidence functions.
  • To extend existing continuous time methods to discrete time scenarios, accommodating different time scales.

Main Methods:

  • Maximum likelihood inference for discrete time cause-specific hazard models.
  • Development of prediction methods for cumulative incidence functions.
  • Derivation of consistent variance estimators for predicted cumulative incidence functions.
  • Utilizing generalized estimating equations for implementation, allowing separate model fitting.

Main Results:

  • Proposed methods provide appropriate statistical inferences for discrete time competing risks data.
  • The methodology effectively handles scenarios with different event types on distinct time scales.
  • Simulation studies confirm the methods perform well in realistic settings.
  • The approach is illustrated using stage III colon cancer data from the SEER program.

Conclusions:

  • The developed methods offer a robust framework for analyzing discrete time competing risks data.
  • These advancements are crucial for accurate epidemiological research, particularly in cancer registries.
  • The methodology extends prior work and is practically implementable using standard statistical software.