Dynamic prediction of time to a clinical event with sparse and irregularly measured longitudinal biomarkers
Dynamic prediction of time to a clinical event with sparse and irregularly measured longitudinal biomarkers
Abstract In clinical research and practice, landmark models are commonly used to predict the risk of an adverse future event, using patients' longitudinal biomarker data as predictors. However, these data are often observable only at intermittent visits, making their measurement times irregularly spaced and unsynchronized across different subjects. This poses …