A Bayesian Joint Model of Multiple Nonlinear Longitudinal and Competing Risks Outcomes for Dynamic Prediction in Multiple Myeloma: Joint Estimation and Corrected Two-Stage Approaches.

ABSTRACT: Predicting cancer-associated clinical events is challenging in oncology. In Multiple Myeloma (MM), a cancer of plasma cells, disease progression is determined by changes in biomarkers, such as serum concentration of the paraprotein secreted by plasma cells (M-protein). Therefore, the time-dependent behavior of M-protein and the transition across lines of therapy (LoT), which may be a consequence of disease progression, should be accounted for in statistical models to predict relevant clinical outcomes. Furthermore, it is important to understand the contribution of the patterns of longitudinal biomarkers, upon each LoT initiation, to time-to-death or time-to-next-LoT. Motivated by these challenges, we propose a Bayesian joint model for trajectories of multiple longitudinal biomarkers, such

SUBMITTER: Alvares D 

PROVIDER: S-EPMC11771571 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature