Individual dynamic prediction for cure and survival based on longitudinal biomarkers

To optimize personalized treatment strategies and extend patients' survival times, it is critical to accurately predict patients' prognoses at all stages, from disease diagnosis to follow-up visits. The longitudinal biomarker measurements during visits are essential for this prediction purpose. Patients' ultimate concerns are cure and survival. However, in many situations, there is no clear biomarker indicator for cure. We propose a comprehensive joint model of longitudinal and survival data and a landmark cure model, incorporating proportions of potentially cured patients. The survival distributions in the joint and landmark models are specified through flexible hazard functions with the proportional hazards as a special case, allowing other patterns such as crossing hazard and survival functions. Formulas are provided for predicting each individual's probabilities of future cure and survival at any time point based on his or her current biomarker history. Simulations show that, with these comprehensive and flexible properties, the proposed cure models outperform standard cure models in terms of predictive performance, measured by the time-dependent area under the curve of receiver operating characteristic, Brier score, and integrated Brier score. The use and advantages of the proposed models are illustrated by their application to a study of patients with chronic myeloid leukemia.