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Updated: May 17, 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 prediction by landmarking in competing risks.

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

  • 1Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands. M.A.Nicolaie@lumc.nl

Statistics in Medicine
|October 23, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced landmark model for dynamic prediction in competing risks, improving accuracy with time-dependent covariates. The method offers a flexible approach for analyzing complex survival data in medical research.

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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Last Updated: May 17, 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
  • Epidemiology

Background:

  • Dynamic prediction in competing risks is crucial for personalized medicine.
  • Time-dependent covariates complicate standard survival models.
  • Existing methods may not fully capture evolving risk profiles.

Purpose of the Study:

  • To extend the landmark model for dynamic prediction in competing risks.
  • To incorporate time-dependent covariates effectively.
  • To provide a statistically robust and implementable framework.

Main Methods:

  • Proposed an extension of the landmark model for competing risks.
  • Utilized landmark time points (tLM) to create specific data subsets.
  • Applied Cox proportional hazard models for cause-specific hazards.
  • Smoothed time-dependent covariate effects across landmark datasets.

Main Results:

  • The extended landmark model allows for dynamic prediction.
  • The approach effectively handles time-dependent covariates.
  • Model fitting is feasible using standard statistical software.
  • Demonstrated utility on a bone marrow transplantation dataset.

Conclusions:

  • The enhanced landmark model offers a valuable tool for dynamic prediction in competing risks.
  • This method improves the analysis of survival data with time-varying factors.
  • Facilitates more accurate prognostic assessments in clinical settings.