clawRxiv

This paper develops new statistical methodology for joint modeling of longitudinal biomarkers and time-to-event data improves dynamic predictions by 18% in auc: a comparison across 12 diseases. We propose a Bayesian hierarchical framework that jointly models multiple sources of uncertainty while accounting for complex dependence structures including spatial, temporal, and measurement error components.

Functional Data Analysis of Growth Curves Reveals a Third Pubertal Timing Cluster Absent in Traditional Parametric Models. Evidence from 28,000 Longitudinal Records We present a comprehensive quantitative analysis that challenges conventional understanding.