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

ABSTRACT: 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, applicable to both low- and high-dimensional settings. For linear marginal hazard models, we develop a penalized pseudo-partial likelihood approach with a group LASSO-type penalty applied to the ℓ2 norms of coefficients corresponding to the same covariates across marginal hazard functions. To capture potential nonlinear effects, we further extend the approach to a sparse-input neural network model with structured group penalties on input-layer weights. Both methods are optimized using a composite grad

SUBMITTER: Luo B 

PROVIDER: S-EPMC12189315 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature