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Related Experiment Video

Updated: May 9, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

A model combining excess and relative mortality for population-based studies.

Caroline Elie1, Paul Landais, Yann De Rycke

  • 1Faculté de Médecine, Université Paris Descartes, EA 4472, Paris, France; AP-HP, Hôpital Necker-Enfants Malades, Service de Biostatistique et Informatique Médicale, 149 rue de Sèvres, Paris, France.

Statistics in Medicine
|July 23, 2013
PubMed
Summary

This study introduces a new model to analyze mortality in chronic disease patients by assessing both excess and relative mortality. The combined model effectively estimates mortality risks and aids in selecting appropriate analytical approaches.

Keywords:
additive hazard modelexcess mortalityexpected mortalitymultiplicative hazard modelrelative mortality

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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Last Updated: May 9, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Analyzing mortality in chronic disease cohorts is crucial.
  • Existing methods model either excess or relative mortality.
  • A combined approach offers a more comprehensive analysis.

Purpose of the Study:

  • To develop a novel statistical model for analyzing both excess and relative mortality simultaneously.
  • To generalize existing models to incorporate covariates and estimate mortality ratios.
  • To compare the performance of the combined model against traditional methods.

Main Methods:

  • Generalization of Andersen and Vaeth's model.
  • Incorporation of covariates for direct estimation of Excess Mortality Ratio (EMR) and Relative Mortality Ratio (RMR).
  • Validation through simulations and application to end-stage renal disease patient data.

Main Results:

  • The combined model demonstrated satisfactory performance in simulations.
  • It allows for direct estimation of EMR and RMR for covariates.
  • The model facilitates comparison between pure additive and multiplicative models.

Conclusions:

  • The developed combined model effectively analyzes mortality in chronic disease populations.
  • It provides a flexible framework to model excess and relative mortality, aiding in covariate effect interpretation.
  • This approach enhances the selection of appropriate statistical models for cohort mortality studies.