Presentation (CTI)

OBJECTIVES: Cancer imposes a growing burden on both patients and healthcare systems. While new therapies contribute to improved survival, they are often associated with high costs. To support evidence-based resource allocation, Health Technology Assessment (HTA) plays a vital role in evaluating new cancer treatments. In oncology, Partitioned Survival Models (PSMs) are commonly used. Increasing HTA collaboration across the Nordics, through the Joint Nordic HTA-Bodies (JNHB), highlights the need for more adaptable tools to support joint evaluations and improve decision-making. The study aimed to determine the essential components and technical implementation needed to build a generic PSM for oncology and assess its feasibility.
METHODS: A targeted literature review identified essential components for inclusion and best practices for technical implementation. Based on these findings, a PSM was built in Microsoft Excel. The model was tested with two datasets differing in healthcare setting and cancer type to assess its generic adaptability. Model validation was carried out using TECH-VER and AdViSHE checklists.
RESULTS: The model incorporated required, recommended, and optional components based on Nordic HTA guidelines. It performed as intended across both datasets, confirming adaptability. Interconnected model components ensured consistent updates across all sheets, while generic cell naming, and a flexible settings sheet enabled seamless adaptation across countries and treatments. Technical validation confirmed its accuracy and robustness.
CONCLUSIONS: This study indicates that a generic and adaptable PSM is feasible for oncology. Validation with two datasets confirmed its flexibility and potential to support equitable access to oncology treatments. By improving transparency, reducing duplication of effort, and supporting consistent economic evaluations, the model supports joint assessment across the Nordics. While further validation is recommended, this work provides a valuable and transferable modelling framework for future use in oncology assessments