Bivariate variable ranking for censored time‐to‐event data via Copula link based additive models

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 .