Dynamic prediction of interval censored time-to-event outcomes using a longitudinal biomarker

Abstract:  This talk discusses methods for using a longitudinal biomarker to dynamically predict an interval-censored time to event outcome. We first investigate a shared random effects model with longitudinal and interval censored survival sub-models. In our motivating clinical example, the biomarker values were highly variable, and the higher the variance meant the patient was likely being non-adherent to treatment. Thus, individual variance of the longitudinal biomarker was thought to be important in prediction of adverse events. The shared random effects model incorporates the sharing of an individual-specific variance component, along with a traditional intercept and slope. Using this model, we develop a dynamic prediction framework to calculate individualized predicted probabilities of event-free survival for new subjects, based on historical biomarker measurements and demographic data.