Online Updating Mixture Cure Models for Large-Scale Survival Data Streams

The growing availability of large-scale survival data streams present significant challenges for statistical inference particularly in the presence of a cure fraction, which complicates estimation and increases computational burden. To address these challenges, we propose an online updating mixture cure model framework. Our approach employs a batchwise renewable estimation procedure that aggregates historical information into summary statistics to update the model without retaining historical individual-level data, and thus eliminating the need for full data storage. A renewable penalized expectation-maximization algorithm is developed to enable simultaneous parameter estimation and variable selection. Furthermore, a tuning-free strategy is incorporated to enhance computational efficiency. The asymptotic properties of the proposed method are established. Simulation studies and an application to breast cancer data from the Surveillance, Epidemiology, and End Results database demonstrate the method's estimation accuracy, predictive performance, and computational scalability.