Bivariate Variable Ranking for censored time-to-event data via Copula Link Based Additive models
Methodology
2024-10-14 v1
Abstract
In this paper, we present a variable ranking approach established on a novel measure to select important variables in bivariate Copula Link-Based Additive Models (Marra & Radice, 2020). The proposal allows for identifying two sets of relevant covariates for the two time-to-events without neglecting the dependency structure that may exist between the two survivals. The procedure suggested is evaluated via a simulation study and then is applied for analyzing the Age-Related Eye Disease Study dataset. The algorithm is implemented in a new R package, called BRBVS..
Cite
@article{arxiv.2410.08382,
title = {Bivariate Variable Ranking for censored time-to-event data via Copula Link Based Additive models},
author = {Danilo Petti and Marcella Niglio and Marialuisa Restaino},
journal= {arXiv preprint arXiv:2410.08382},
year = {2024}
}
Comments
51 pages, 4 Figures