A Review of NICE Technology Appraisals of Oncology Treatments in the Past 2 Years – Model Approaches, End of Life Status, and Recommendation Decisions

OBJECTIVES: Oncology is one of the most important therapeutic areas for reviews by the National Institute for Health and Care Excellence (NICE). Our aim was to understand the commonly used model approaches, the impact of end of life status, and the alignment between cost-effectiveness estimates and recommendation decisions.

METHODS: Technical appraisals (TAs) of oncology drugs published by NICE between 1^st June 2021 and 25^st May 2023 were reviewed and analysed.

RESULTS: Information was extracted from 66 TAs. Non-small-cell lung cancer was the most common indication submitted (14 TAs), followed by breast cancer (10 TAs) and lymphoma (6 TAs). 64 TAs used cost-effectiveness analysis (CEA) or cost-utility analysis (CUA), while the other 2 TAs used cost- minimization analysis (CMA). Partitioned survival models (PSMs) were the most commonly used model approach (39 TAs, 61%), followed by Markov and semi-Markov models (12 TAs, 18%). No drug met highly specialised technologies (HST) criterion. 30 drugs (45%) met end of life criterion, in which 22 drugs (73%) were recommended with all incremental cost-effectiveness ratios (ICERs) under £50,000 per quality-adjusted life year (QALY) gained threshold. For the other 36 drugs (55%), 31 drugs (86%) were recommended with all ICERs under £20,000 to £30,000 per QALY gained threshold. ICERs of the remaining drugs (13 TAs, 20%) were either above the threshold or very uncertain: 6 drugs were not recommended, 6 drugs were recommended in the Cancer Drugs Fund, and 1 drug was recommended with managed access.

CONCLUSIONS: PSM became the most commonly used model approach in oncology health economic models in the past 2 years. Nearly half of the oncology drugs met end of life criterion and were judged against the £50,000 per QALY gained threshold. NICE recommendation decisions of the oncology TAs were fully aligned with published ICER thresholds, suggesting an important role for value-based pricing.