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arXiv · 2410.08382

Bivariate Variable Ranking for censored time-to-event data via Copula Link Based Additive models

Danilo Petti·Marcella Niglio·Marialuisa Restaino
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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..

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Danilo Petti, Marcella Niglio, Marialuisa Restaino. 2024-10-10. Bivariate Variable Ranking for censored time-to-event data via Copula Link Based Additive models. https://arxiv.org/abs/2410.08382

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