… Our objective was thus to propose a solution for the dynamic prediction of a health event …
from large dimensional longitudinal biomarker data. Computing dynamic predictions in the …

… to conduct dynamic prediction, which accommodates multiple longitudinal biomarker processes.
… distribution or a specific model for each longitudinal data process. Instead, we apply the …

… to predict the risk of clinical events, such as disease progression or death, using these
longitudinal biomarkers as … The prediction is dynamic in the sense that it can be made at any time …

… We also assess the model’s ability to dynamically predict a patient’s probability of developing
dnDSA after surviving dnDSA-free for a specified period. This is motivated by the clinical …

… models the multiple biomarkers with a shared latent reduced rank longitudinal principal …
dynamic prediction of the event time. The proposed joint model for highly correlated biomarkers …

… We propose an alternative approach for dynamic prediction using random survival forests
in a landmarking framework. With a simulation study, we compared the predictive performance …

… We illustrate its use for dynamic prediction in an application to predicting death for heart
valve transplant patients using longitudinal left ventricular mass index information. …

… biomarker discovery and dynamic risk prediction. The goal of this article is to provide simple
and … tools for dynamically predicting risk when the number of longitudinal biomarkers is large …

… , we show how to obtain dynamically updated subject-specific risk predictions for new patients
at each time additional measurements of the longitudinal biomarker are recorded. We first …

… Let us assume that we are interested in the longitudinal biomarker … longitudinal biomarker.
Furthermore, by using multiple biomarkers in the proposed model, the dynamic predictions of …