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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

Dynamic pseudo-observations: a robust approach to dynamic prediction in competing risks.

M A Nicolaie1, J C van Houwelingen, T M de Witte

  • 1Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, P.O. Box 9600, 2300 RC, Leiden, The Netherlands.

Biometrics
|July 20, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel dynamic prediction method for survival data with competing risks, using dynamic pseudo-observations to estimate covariate effects directly on cumulative incidence. This approach offers robustness and computational flexibility for improved predictions.

Keywords:
Competing risksDynamic predictionDynamic pseudo-observationQuasi-likelihoodWorking correlation

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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

Related Experiment Videos

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
  • Survival Analysis
  • Medical Statistics

Background:

  • Dynamic prediction of survival data with competing risks is challenging.
  • Existing methods often rely on complex cause-specific or subdistribution hazard models.
  • Accurate estimation of covariate effects on cumulative incidence is crucial.

Purpose of the Study:

  • To propose a new, robust approach for dynamic prediction in competing risks survival data.
  • To extend the landmark model for improved estimation of covariate effects.
  • To provide accurate dynamic predictions and reliable standard errors.

Main Methods:

  • Introduction of dynamic pseudo-observations based on prediction probabilities at landmark times.
  • Utilizing a flexible generalized linear model with dynamic pseudo-observations.
  • Employing generalized estimation equations for estimating baseline and covariate effects.
  • Focusing directly on prediction probabilities, avoiding complex hazard modeling.

Main Results:

  • The proposed method allows direct estimation of covariate effects on the cumulative incidence scale.
  • Dynamic predictions and robust standard errors are achieved.
  • The approach is computationally efficient and can be fitted using existing statistical software.
  • Demonstrated on chronic myeloid leukemia patient data.

Conclusions:

  • The novel method provides a robust and flexible alternative for dynamic prediction in competing risks.
  • It simplifies the modeling process by focusing on prediction probabilities.
  • The approach is applicable to real-world clinical datasets, enhancing prognostic accuracy.