Background: Understanding health-related quality of life (HRQoL) dynamics is essential for assessing and improving treatment experiences; however, clinical and observational studies struggle to capture their full complexity. We use simulation modeling and the case of Chimeric Antigen Receptor T-cell therapy-a type of cancer immunotherapy that can prolong survival, but carries life-threatening risks-to study HRQoL dynamics.
Methods: We developed an exploratory system dynamics model with mathematical equations and parameter values informed by literature and expert insights. We refined its feedback structure and evaluated its dynamic behavior through iterative interviews. Model simulated HRQoL from treatment approval through six months post-infusion. Two strategies-reducing the delay to infusion and enhancing social support-were incorporated into the model. To dynamically evaluate the effect of these strategies, we developed four metrics: post-treatment HRQoL decline, recovery time to pre-treatment HRQoL, post-treatment HRQoL peak, and durability of the peak.
Results: Model captures key interactions within HRQoL, providing a nuanced analysis of its continuous temporal dynamics, particularly physical well-being, psychological well-being, tumor burden, receipt and efficacy of treatment, side effects, and their management. Model analysis shows reducing infusion delays enhanced HRQoL across all four metrics. While enhanced social support improved the first three metrics for patients who received treatment, it did not change durability of the peak.
Conclusions: Simulation modeling can help explore the effects of strategies on HRQoL while also demonstrating the dynamic interactions between its key components, offering a powerful tool to investigate aspects of HRQoL that are difficult to assess in real-world settings.
Keywords: CAR T-cell therapy; Cancer immunotherapy; Health-related quality of life; Over-time dynamics; Simulation modeling.
Understanding how treatments affect patients’ quality of life over time is crucial, but capturing the complex interactions of health factors poses a challenge for clinical and observational research. To overcome this, we have turned to simulation modeling, a method that allows for a more thorough exploration of these dynamics. Our study focuses on cancer immunotherapy, a treatment that, despite its potential to prolong survival, also comes with life-threatening risks. We evaluated the effectiveness of two strategies aimed at improving quality of life: reducing the time to treatment infusion and enhancing social support. These strategies were assessed across three different patient scenarios: those not initially eligible for treatment, patients experiencing a relapse, and patients showing a complete response. By using simulation modeling, we demonstrated how this approach can help explore the dynamics and interactions of various health factors and the impact of specific strategies.
© 2024. The Author(s).