Methodological study demonstrates a copula-based variable ranking algorithm for paired time-to-event outcomes, highlighting improved covariate selection accounting for event dependency.
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 .
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Petti et al. (2026) studied this question.
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