Modeling Bivariate Survival with Dependent Censoring Using Copulas

Music City Center 

Independent censoring is a key assumption usually made when analyzing time-to-event data. However, this assumption is untestable and can be problematic, particularly in studies with disproportionate loss to follow-up due to adverse events. This paper addresses the challenges associated with dependent censoring by introducing a likelihood-based approach for analyzing bivariate survival data under dependent censoring. A flexible Joe-Hu copula is used to formulate the interdependence within the quadruple times (two events
and two censoring times). The marginal distribution of each event or censoring time is modeled via the Cox proportional hazards model. Our estimator possesses consistency and desirable asymptotic properties under regularity conditions. We provide results under extensive simulations with application to prostate cancer data.

Archimedean copula

Bivariate Survival

Dependent Censoring

Joe-Hu copula

Joint survival

Prostate Cancer Survival 

Section on Statistics in Epidemiology