Use of external data to inform overall survival extrapolation in NICE technology appraisals for oncology drugs. - Petitjean, Audrey; Shang, Huiyu; Bullement, Ash; Latimer, Nicholas (Journal of Medical Economics, 2025)
Aim: To assess use of external evidence for overall survival (OS) estimation in oncology single-technology appraisals (STAs) by the National Institute for Health and Care Excellence (NICE). Methods: STAs for oncology drugs appraised by NICE between January 2021 and March 2023 were identified. For each eligible STA, OS extrapolation methods used, the rationale for using external data, the source and type of data, and information on acceptance by the evidence review group (ERG) and the appraisal committee were extracted. Results: Initially, 215 STAs were identified, of which 82 were eligible for the study. Of these, 32 STAs used external data for OS extrapolation, including trial data (44%), real-world data (47%), clinical opinion (25%), meta-analysis (1%) and previous STA (1%). External data were used more frequently in state-transition models for post-event transitions and cure assumptions, and in partitioned-survival models to replace pivotal trial OS, inform long-term survival estimates or to estimate OS based on surrogacy analysis. Sensitivity analyses on use of external data was explored in 16 (50%) of the STAs. The committee accepted use of external data in half of the analysed STAs, acknowledging uncertainty in OS extrapolation. Limitations: The analysis was limited to the STAs published between 2021 and 2023 and publicly available materials on the NICE website. Conclusion: This study provides an overview of external data used to estimate OS in oncology STAs conducted by NICE in recent years. External data, including trial data, real-world data and clinical opinions, were incorporated into recent oncology STAs at various modelling stages. ERGs and appraisal committees were generally accepting of the use of external data. However, it is crucial to conduct a sensitivity analysis and provide a justification for the methods and data source selection.