Longitudinal and time-to-event modeling for the survival of advanced pancreatic ductal adenocarcinoma patients

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers especially at advanced stage. In order to analyze the dynamics of potential prognostic biomarkers and further quantify their relationships with the overall survival (OS) of advanced PDAC patients, we herein developed a parametric time-to-event (TTE) model integrated with longitudinal submodels. Data from 104 patients receiving standard chemotherapies were retrospectively collected for model development, and other 54 patients were enrolled as external validation. The longitudinal submodels were developed with the time-course data of sum of longest diameters (SLD) of tumors, serum albumin (ALB) and body weight (BW) using nonlinear mixed effect models. The model-derived metrics including model parameters and individual predictions at different time points were further analyzed in the TTE model, together with other baseline information of patients. A linear growth-exponential shrinkage model was employed to describe the dynamics of SLD, while logistic models were used to fit the relationship of time prior to death with ALB and BW. The TTE model estimated the ALB and BW changes at the 9th week after chemotherapies as well as the baseline CA19-9 level that showed most significant impact on the OS, and the model-based simulations could provide individual survival rate predictions for patients with different prognostic factors. This study quantitatively demonstrates the importance of physical status and baseline disease for the OS of advanced PDAC patients, and highlights that timely nutrition support would be helpful to improve the prognosis.