Statistica Neerlandica· 2026Q2
Bivariate variable ranking for censored time‐to‐event data via Copula link based additive models
- 1citations
- Q2SCImago
- 2026year
Short summary
A new method ranks variables for two dependent time-to-event outcomes, identifying distinct sets of important covariates for each outcome while accounting for their interdependency.
AI-generated from the title and abstract; the full text is not read.
Key points
- Presents a new variable ranking method for bivariate time-to-event data.
- Identifies separate sets of important covariates for each of the two time-to-event outcomes.
- Accounts for the dependency structure between the two survival times.
- Validated through simulation studies and application to the Age-Related Eye Disease Study dataset.
- Implemented in a new R package named BRBVS.
AI-generated from the title and abstract; the full text is not read.
Abstract
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 .
The authors' abstract, as published at the source. Statistica Neerlandica, 2026 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics