  Often in Phase 3 clinical trials measuring a long-term time-to-event
endpoint, such as overall survival or progression-free survival, investigators
also collect repeated measures on biomarkers which may be predictive of the
primary endpoint. Although these data may not be leveraged directly to support
early stopping decisions, can we make greater use of these data to increase
efficiency and improve interim decision making? We present a joint model for
longitudinal and time-to-event data and a method which establishes the
distribution of successive estimates of parameters in the joint model across
interim analyses. With this in place, we can use the estimates to define both
efficacy and futility stopping rules. Using simulation, we evaluate the
benefits of incorporating biomarker information and the affects on interim
decision making.
