<p>Independent censoring is a key assumption usually made when analyzing time-to-event data. However, this assumption is difficult to assess 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/censoring time is modeled via the Cox proportional hazards model. Our estimator possesses consistency and desirable asymptotic properties under regularity conditions. We present results from extensive simulation studies and further illustrate our approach using prostate cancer data.</p>