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

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

GEE for multinomial responses using a local odds ratios parameterization.

Anestis Touloumis1, Alan Agresti, Maria Kateri

  • 1EMBL-European Bioinformatics Institute, Hinxton, U.K.

Biometrics
|June 4, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new generalized estimating equations (GEE) method for analyzing correlated multinomial data. The proposed approach improves parameter estimation accuracy, especially with time-varying covariates and strong correlations.

Keywords:
Association modelsGeneralized estimating equationsLocal odds ratiosLongitudinal data analysisMultinomial responses

Related Experiment Videos

Last Updated: May 10, 2026

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

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Generalized Estimating Equations (GEE) are commonly used for correlated data.
  • Standard GEE methods may struggle with joint estimation of marginal parameters and dependence structures in multinomial outcomes.
  • Local odds ratios offer a flexible way to model dependence for both ordinal and nominal multinomial responses.

Purpose of the Study:

  • To propose a novel GEE approach for correlated ordinal or nominal multinomial responses.
  • To address limitations of standard GEE in jointly estimating marginal parameters and dependence structures.
  • To utilize local odds ratios for modeling complex dependence patterns.

Main Methods:

  • Developed a GEE approach using a local odds ratios parameterization.
  • Treated the working association vector (α) as a nuisance parameter defining the local odds ratios structure.
  • Employed Goodman's association models to estimate α and approximate dependence structures.
  • Applied a marginal cumulative probit model for analysis.

Main Results:

  • The proposed GEE method demonstrated less bias and higher efficiency compared to the independence working model.
  • These improvements were particularly notable in simulations with time-varying covariates and strong correlations.
  • The method effectively handles the joint existence of marginal parameter and dependence structure estimates.

Conclusions:

  • The novel GEE approach provides a robust method for analyzing correlated multinomial data.
  • It offers improved estimation accuracy over traditional methods, especially in complex scenarios.
  • The local odds ratios parameterization effectively captures dependence structures in both ordinal and nominal data.