University of Washington - Department of Biostatistics
Often in clinical trials where the primary endpoint is the time to an event, patients are also monitored longitudinally with respect to one or more biologic endpoints throughout the follow-up period. Models for characterizing the relationship between the longitudinal measures and the survival outcome (commonly referred to as joint longitudinal and survival models) have recently gained popularity in the statistical literature. In this talk, we will review some recent developments in joint longitudinal and survival modeling, focusing on extensions to basic models that include relaxing either the distributional assumptions for the longitudinal model or the proportional hazards assumptions for the survival model. These models will be illustrated with applications from HIV and cancer vaccine clinical trials.