Investigating the performance of survival prediction using longitudinal biomarker

Music City Center 

Predicting survival remains a critical challenge for many diseases where more traditional statistical models often rely on baseline demographics or disease characteristics. Studying the trajectory of specific biomarkers is important for understanding the dynamic of disease progression and clinical outcome that could predict the overall survival (OS). This project investigated the performance of survival prediction using the trajectory of a longitudinal biomarker (serum protein electrophoresis, SPEP), which captures the dynamic of disease progression over time. Using area under the curve (AUC) and prediction error (PE), model performance was evaluated for several joint models and Cox models for overall survival that considered observed or Bayesian derived clinical data. Using simulated and real data from a Multiple Myeloma study, our findings indicate that joint model incorporating the trajectory of SPEP data improves the predictions for OS.

Longitudinal biomarker

Survival prediction

Joint Model

Oncology clinical trials 

Biopharmaceutical Section