Variable Selection for Multivariate Failure Time Data via Regularized Sparse-Input Neural Network

Variable Selection for Multivariate Failure Time Data via Regularized Sparse-Input Neural Network

This study addresses the problem of simultaneous variable selection and model estimation in multivariate failure time data, a common challenge in clinical trials with multiple correlated time-to-event endpoints. We propose a unified framework that identifies predictors shared across outcomes, applic...

Full description

Saved in:
Bibliographic Details
Main Authors: Bin Luo, Susan Halabi
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Bioengineering
Subjects:
multivariate failure time
variable selection
high dimensionality
group LASSO
non-convex penalty
Online Access:https://www.mdpi.com/2306-5354/12/6/596
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.mdpi.com/2306-5354/12/6/596

Similar Items