Abstract

Joint models for longitudinal and time-to-event data are useful in situations where an association exists between a longitudinal marker and an event time. Examples are abundant in cancer trials as well as in degradation studies in reliability applications. In such contexts, separate models for the longitudinal and survival components that do not take into account the dependence produce inefficient results and are prone to bias. Models and methodologies for studying the longitudinal and the time-to-event processes simultaneously have been an active area of research for the past few decades. A lion’s share of research has been focused on studies where the underlying event occurrence process is governed by a single source of failure/death. Frequently however, one encounters time-to-event mechanism where the event is triggered at the onset of the earliest of multiple potential risks. Joint inference under such competing risks framework have almost exclusively been investigated through models of cause-specific hazards. By contrast, this talk will present some reports on joint models based on latent failure times. We shall carry out our investigation under a Bayesian framework which is naturally suited for joint models that are inherently hierarchical in nature. The methodology will be implemented on an example dataset and will be supplemented by findings from extensive simulations.