A multistate transition model for survival estimation in randomized trials with treatment switching and a cured subgroup

Although many methods have been proposed on the overall survival estimation in randomized trials permitting treatment switching after the progressive disease (PD), the cured subgroup of patients within these trials has not been fully considered. These cured patients would never experience PD and subsequent risk of treatment switching, yet they may suffer death hazard similar to those without the disease. Due to the mix of the cured subgroup, existing methods may yield biased effect estimation for the uncured patients between treatment groups. To address this limitation, we propose a multistate transition model that integrates multi-states of the cure, PD, treatment switching, and death during trials. In this model, the cure probability for all the patients and the death hazard of the cured subgroup are modeled separately. Meanwhile, the semi-competing risks model is used for the treatment effect evaluation on the uncured patients through transitional hazards between states of PD, treatment switching, and death. The particle swarm optimization algorithm is employed to estimate the model parameters. Extensive simulation studies have been conducted to assess the performance of the proposed multistate model in comparison with existing treatment switching adjustment methods. The results show that the treatment effect estimations of our proposed model are more accurate across all scenarios. Moreover, the illustration based on a simulated diffuse large B-cell lymphoma trial demonstrates the applicability and advantages of the proposed model. The robustness of the proposed multistate transition model enables it to accurately estimate the treatment effect in trials that involve a cured subgroup and the treatment switching after PD.