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Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

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Published on: October 23, 2020

Model selection in competing risks regression.

Deborah Kuk1, Ravi Varadhan

  • 1Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA. kukd@mskcc.org

Statistics in Medicine
|February 26, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces new stepwise regression methods for competing risks data, improving model selection for the Fine and Gray model. These methods enhance the analysis of time-to-event data with multiple event types.

Keywords:
AICBICFine and Gray modelcompeting riskscumulative incidencestepwise regression

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Competing risks complicate standard survival analysis.
  • The Fine and Gray model is widely used but lacks robust model selection methods.
  • Existing methods for Cox proportional hazards models are not directly applicable to competing risks.

Purpose of the Study:

  • To develop and evaluate stepwise regression procedures for model selection in the Fine and Gray competing risks model.
  • To introduce BICcr, a new selection criterion tailored for competing risks data with right censoring.
  • To assess the performance of these new methods through simulation and a real-world application.

Main Methods:

  • Development of forward and backward stepwise regression procedures.
  • Utilized AIC, BIC, and the newly proposed BICcr as selection criteria.
  • Conducted a large-scale simulation study to evaluate performance.
  • Applied the methods to the Women's Health Initiative-Observational Study data.

Main Results:

  • The developed stepwise regression procedures performed well in the simulation study.
  • The methods successfully identified important predictors in the competing risks analysis.
  • Bone mineral density was assessed for hip fracture risk prediction against mortality as a competing risk.

Conclusions:

  • The new stepwise selection methods provide effective tools for the Fine and Gray model.
  • The freely available R package 'crrstep' implements these validated procedures.
  • These advancements aid researchers in analyzing complex time-to-event data with competing risks.