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Related Experiment Video

Updated: Jun 23, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Gradient lasso for Cox proportional hazards model.

Insuk Sohn¹, Jinseog Kim, Sin-Ho Jung

¹Department of Biostatistics & Bioinformatics, Duke University, NC 27705, USA.

Bioinformatics (Oxford, England)

|May 19, 2009

Summary

This study introduces the gradient lasso algorithm for penalized Cox regression, effectively addressing high-dimensional gene expression data challenges. The method demonstrates faster convergence and competitive performance in predicting survival phenotypes.

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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Published on: October 23, 2020

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Last Updated: Jun 23, 2026

An R-Based Landscape Validation of a Competing Risk Model

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

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

High-dimensional gene expression data presents collinearity issues for survival prediction models like Cox's proportional hazards.
Existing penalized Cox models face computational challenges, including slow convergence due to matrix inversions.

Purpose of the Study:

To implement a penalized Cox regression with a lasso penalty using the gradient lasso algorithm.
To overcome computational limitations of existing methods for high-dimensional survival data analysis.

Main Methods:

Utilized the gradient lasso algorithm for penalized Cox regression.
Applied the method to simulation studies and real-world datasets (diffuse large B-cell lymphoma, breast cancer).

Main Results:

The gradient lasso algorithm demonstrated faster convergence to the global optimum.
Simulation studies confirmed the recovery of prognostic genes.
Performance was competitive with existing methods in terms of computational time, prediction accuracy, and selectivity.

Conclusions:

The gradient lasso algorithm offers an efficient and reliable tool for developing prediction models with high-dimensional covariates, including gene expression data.
The R package glcoxph is available for practical application.