Modeling times to multiple events under informative censoring with C-vine copula

Music City Center 

The study of times to nonterminal events of different types and their interrelation is a compelling area of interest. The primary challenge in analyzing such multivariate event times is the presence of informative censoring by the terminal event. While numerous statistical methods have been proposed for a single nonterminal event, i.e., semi-competing risks data, there remains a dearth of tools for analyzing times to multiple nonterminal events due to their more complex dependence structures. We introduce a novel modeling framework that leverages the vine copula to capture the heterogeneous dependence between each pair of event times in terms of structure and strength. Furthermore, our model allows for regression modeling for all the marginal distributions of times to nonterminal and terminal events. We propose a likelihood-based estimation and inference procedure, which can be implemented efficiently in subsequent stages. Through simulation, we demonstrate the superiority of our approach over existing methods. We also apply our approach to data from a crowdfunding platform to investigate the relationship between creator-backer communication activities and a creator's lifetime.

Graphical models

informative censoring

multiple event times

stage-wise estimation

vine copula 

Lifetime Data Science Section