Cure rate joint model for time-to-event data and longitudinal tumor burden with potential change points

In non-small cell lung cancer (NSCLC) clinical trials, tumor burden (TB) is a key longitudinal biomarker for assessing treatment effects. Typically, standard-of-care (SOC) therapies and some novel interventions initially decrease TB; however, many patients subsequently experience an increase-indicating disease progression-while others show a continuous decline. In patients with an eventual TB increase, the change point marks the onset of progression and must occur before the time of the event. To capture these dynamics, we propose a cure-rate joint model that integrates time-to-event and longitudinal TB data and distinguishes change-point and non-change-point trajectory groups. For the change-point group, our approach flexibly estimates an individualized change point by leveraging time-to-event information. We use a Monte Carlo Expectation-Maximization (MCEM) algorithm for efficient parameter estimation. Simulation studies show that our model can recover diverse disease progression patterns and yield robust marginal TB outcome estimates while accommodating censoring complexity. When applied to a Phase 3 NSCLC trial comparing cemiplimab monotherapy to SOC, the treatment group demonstrates delayed tumor regrowth and consistently lower TB over time, highlighting the clinical utility of our approach. The implementation code is publicly available on https://github.com/quyixiang/JoCuR.