Presentation (CTI)

OBJECTIVES: Although cost-effectiveness models for different cancer types often share similar methodological approaches, these models are typically developed separately for each disease area. Developing a flexible and comprehensive modelling platform that can represent heterogeneous patient trajectories across different cancer types may support more consistent and transferable cost-effectiveness analyses in oncology.
METHODS: Three EORTC studies provided the basis for this work: LEGATO phase III trial in glioblastoma, comparing lomustine plus reirradiation with lomustine alone; STREXIT2 observational study in soft tissue sarcoma, comparing neoadjuvant chemotherapy followed by surgery with surgery alone; DE-ESCALATE phase III trial in prostate cancer, comparing intermittent versus continuous intensified androgen deprivation therapy. First, systematic literature reviews were conducted to identify previously published economic models in the relevant target patient populations. Next, we reviewed the study protocols and described the trajectory of care in each disease area to identify clinical events, treatment sequences, and outcomes relevant to economic modelling. A conceptual model structure was developed to reflect both the design of each study, the involved treatments and the clinical management of the disease area.
RESULTS: The conceptual model design established an individual patient discrete event simulation (DES) framework for the three EORTC studies. The DES approach was selected to capture heterogeneous patient trajectories in targeted cancer types, including treatment initiation and discontinuation, disease progression, adverse events, treatment switching, or mortality. Events are modelled probabilistically using statistical distributions informed by trial data, published literature, and other evidence sources, enabling variability in patient experiences and outcomes to be represented over time. The model is being implemented in Excel VBA, with planned transition to Python and development of a web-based platform.
CONCLUSIONS: This framework provides a flexible basis for assessing the economic value of alternative oncology treatment strategies across different types of cancer. It may support future healthcare decision-making and policy evaluations.