Dynamic Prediction of Survival Outcomes Using Longitudinal Biomarkers

─Joint Model and Landmarking─

Abstract

Dynamic prediction estimates the survival probability, conditioned on a subject not yet experiencing the event of interest at a specific time point. To improve the accuracy of dynamic prediction, one can incorporate baseline measurements and biomarkers measured during a follow-up. In this review article, we focus on two predominant approaches: joint modeling and landmarking. Joint modeling specifies the joint distribution of the biomarkers and the event times. Landmarking originally utilizes only the biomarkers at the onset of the start time point of prediction, but recent versions have begun to incorporate data observed after the prediction time point. We illustrate how the two approaches predict the survival probability and subsequently demonstrate the application of these methods through a primary biliary cholangitis dataset with the R codes provided.