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Updated: Jun 16, 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

Vertical modeling: a pattern mixture approach for competing risks modeling.

M A Nicolaie1, Hans C van Houwelingen, H Putter

  • 1Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, P.O. Box 9600, 2300 RC, Leiden, The Netherlands. m.a.nicolaie@lumc.nl

Statistics in Medicine
|January 26, 2010
PubMed
Summary
This summary is machine-generated.

Vertical modeling offers a novel approach to competing risks analysis by decomposing failure time and cause. This statistical method provides more efficient cumulative incidence estimates than traditional models.

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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Last Updated: Jun 16, 2026

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

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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:

  • Competing risks analysis is crucial for understanding multiple failure causes.
  • Traditional nonparametric models may lack statistical efficiency in estimating cumulative incidences.
  • A new decomposition of failure time and cause is proposed.

Purpose of the Study:

  • To introduce and evaluate vertical modeling as an alternative estimation approach in competing risks.
  • To demonstrate the statistical efficiency of vertical modeling compared to standard methods.
  • To apply the vertical modeling technique to a real-world clinical dataset.

Main Methods:

  • Vertical modeling decomposes the joint distribution into time of failure and cause of failure conditional on time.
  • Observable quantities like total hazard and relative cause-specific hazards are utilized.
  • Relative cause-specific hazards are estimated using multinomial logistic regression and smoothing splines.
  • Implementation is feasible with standard statistical software.

Main Results:

  • Vertical modeling provides statistically more efficient estimates of cumulative incidences.
  • The method was illustrated using data from 8966 leukemia patients.
  • The approach leverages observable quantities for robust estimation.

Conclusions:

  • Vertical modeling is a viable and statistically efficient alternative for competing risks estimation.
  • The method offers improved precision in cumulative incidence estimates.
  • This approach has practical applications in clinical and epidemiological research.