BACKGROUND: The evaluation of innovative oncology medicines presents significant challenges related to the selection of appropriate clinical endpoints and the sufficiency of evidence, particularly in therapeutic areas, such as immunotherapies, targeted treatments, single-arm trials, and tumor-agnostic therapies. In these contexts, the added value of new treatments is often not adequately captured by traditional clinical trial endpoints, such as overall survival (OS), which remains the gold standard in oncology assessment. This article aims to analyze the main sources of uncertainty in the value assessment of oncology therapies in Spain, with a focus on the limitations of current endpoints and evidence-generation processes, and to provide recommendations for enhancing the recognition and assessment of additional clinical benefit. METHODS: A multidisciplinary expert panel composed of twelve professionals, including medical oncologists, hospital pharmacists, health economists, and patient representatives, was convened to identify and discuss key sources of uncertainty in the value assessment of oncology treatments, particularly those related to clinical endpoint selection and evidence generation. The panel participated in three structured plenary sessions. Additional external experts were engaged to provide complementary input in areas, such as tumor-specific characteristics and statistical methodology. Consensus statements were developed through an iterative process of discussion, critical appraisal, and refinement across and between sessions. RESULTS: The expert panel issued twelve recommendations to improve value assessment in oncology. These include tailoring clinical endpoints to treatment type, tumor characteristics, and stage; complementing overall survival with milestone analysis and quality-of-life measures; and standardizing real-world evidence collection across the healthcare system. The panel advocated for a national portfolio of prioritized endpoints, appropriate statistical methods by context, and the conditional use of early-phase data for decision-making. Additional recommendations addressed the use of synthetic control arms, flexible reimbursement models, advanced analytics (e.g., AI and Big Data), evaluator expertise, and the promotion of stakeholder training and transparency. CONCLUSIONS: Addressing the challenges of clinical endpoint selection and evidence generation is essential to reduce uncertainty in the value assessment of innovative oncology treatments. The twelve expert recommendations outlined in this study provide a structured roadmap to improve methodological consistency, enhance the relevance and robustness of clinical and real-world data, and promote a more adaptive and transparent evaluation framework. These proposals aim to support more evidence-based, equitable, and sustainable decision-making within the Spanish healthcare system, while aligning with broader European initiatives in oncology drug assessment.
García-Campelo et al. (Wed,) studied this question.