This thesis analyzes censored data in recurrent event, longitudinal, and survival settings. In Chapter 2, a straightforward, flexible methodology is proposed to estimate parameters indexing the conditional means and variances of the interevent times in a recurrent event process. In Chapter 3, we analyze discretely and informatively observed multivariate continuous longitudinal data; missingness and terminal events are introduced in Chapter 4. In Chapters 3 and 4, the inter-event times are considered a nuisance and the goal is to estimate parameters driving the longitudinal process. To do this, we propose an innovative conditional estimating equation that can model individual trajectories. Finally, Chapter 5 uses these subject-specific trajectories to estimate parameters indexing the terminal event process and predict future survival for arbitrary subjects.