In this article, we present a variable ranking approach based on a novel measure for selecting important variables in bivariate Copula Link-Based Additive Models [30]. 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 survival times. The suggested procedure is evaluated through a simulation study, and then applied to analyze the Age-Related Eye Disease Study dataset. The algorithm is implemented in a new R package, called BRBVS.
Bivariate variable ranking for censored time-to-event data via Copula link based additive models
DAnilo Petti
;Marcella Niglio
;Marialuisa Restaino
2026
Abstract
In this article, we present a variable ranking approach based on a novel measure for selecting important variables in bivariate Copula Link-Based Additive Models [30]. 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 survival times. The suggested procedure is evaluated through a simulation study, and then applied to analyze the Age-Related Eye Disease Study dataset. The algorithm is implemented in a new R package, called BRBVS.File in questo prodotto:
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