Presentation (CTI)

OBJECTIVES: NICE technology appraisals (TAs) evaluate cost-effectiveness of interventions at individual decision points in a treatment pathway. However, the treatment sequence generated from a series of appraisals may differ from the optimal sequence estimated for the whole pathway. NICE commissioned a pilot to assess a “pathways approach” for evaluating treatment sequences in advanced non-small cell lung cancer (NSCLC). Existing model structures to evaluate pathways require patient-level simulation or use numerous tunnel states, with potential operational barriers around data access, ease of use and interpretation. We aimed to develop a novel model structure for evaluation of oncology treatment sequences to overcome these challenges. The model was designed in Microsoft Excel for stakeholder accessibility.
METHODS: A novel ‘nested partitioned survival’ model structure was conceptualised to evaluate treatment sequences in oncology pathways. The structure builds on principles of partitioned survival models, with new methods to estimate line-specific transition probabilities, costs and QALYs. The structure was used to develop a pilot model which covered a NSCLC pathway with multiple treatment lines and subgroups based on histology and PD-L1 expression. The model was informed by network meta-analyses, previous TAs and real-world evidence.
RESULTS: The ‘nested partitioned survival’ structure was developed into a fully-executable model evaluating treatments in a complex NSCLC pathway. The cohort model structure circumnavigated the need for patient-level data without using tunnel states. The model had probabilistic and deterministic functionality. The model showed the most optimal treatment sequence for patients and the most cost-effective treatment at each node. List price net monetary benefits fell between -£87,335 and £14,214. The model included placeholders for additional genetic mutation subgroups to enable continued use as the NSCLC pathway evolves.
CONCLUSIONS: The novel structure for evaluating treatment sequences in oncology pathways has potential to improve cost-effectiveness decisions about treatment sequences, and has potential to support future development of oncology reference models.