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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

Boosting for high-dimensional time-to-event data with competing risks.

Harald Binder1, Arthur Allignol, Martin Schumacher

  • 1Freiburg Center for Data Analysis and Modeling, University of Freiburg, Eckerstr 1 and Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, Freiburg, Germany. binderh@fdm.uni-freiburg.de

Bioinformatics (Oxford, England)
|February 27, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new boosting method for high-dimensional time-to-event data with competing risks. The approach enhances prediction accuracy by simultaneously analyzing the event of interest and competing events, incorporating microarray data and clinical covariates.

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An R-Based Landscape Validation of a Competing Risk Model
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Last Updated: Jun 25, 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

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

Area of Science:

  • Biostatistics
  • Computational Biology
  • Medical Informatics

Background:

  • Analyzing high-dimensional time-to-event data with competing risks requires specialized models that handle simultaneous event analysis and censoring.
  • Existing low-dimensional proportional hazards models for subdistribution hazards lack adaptation for high-dimensional settings.
  • Effective tools for evaluating the predictive performance of these complex models are needed.

Purpose of the Study:

  • To develop a novel boosting approach for fitting proportional subdistribution hazards models in high-dimensional settings.
  • To provide robust methods for assessing the prediction performance of these models in competing risks scenarios.
  • To address the need for simultaneous analysis of the event of interest and competing events, alongside censoring.

Main Methods:

  • A boosting algorithm is proposed for fitting proportional subdistribution hazards models to high-dimensional data.
  • The method incorporates a large number of features, such as microarray data, along with clinical covariates.
  • Prediction performance is evaluated using bootstrap.632+ estimates of prediction error curves, specifically adapted for competing risks.

Main Results:

  • The boosting approach successfully fits proportional subdistribution hazards models for high-dimensional time-to-event data.
  • Bootstrap.632+ estimates provide reliable evaluation of prediction error curves in the competing risks setting.
  • Application to bladder cancer microarray data demonstrates the model's ability to assess the added predictive value of microarray measurements when considering competing events.

Conclusions:

  • The proposed boosting method offers a viable solution for analyzing high-dimensional time-to-event data with competing risks.
  • The implemented R packages, CoxBoost and peperr, provide accessible tools for researchers.
  • Simultaneous consideration of competing events enhances the judgment of predictive power from high-dimensional data like microarrays.