Towards meaningful outcome measures for health economic evaluations in skin cancer : A decision matrix to guide transparent outcome selection in skin cancer health economic evaluations, supporting more informed decision-making.

Healthcare systems are facing increasing resource constraints, so robust health economic evaluations (HEE) are essential to inform evidence-based decision-making, also in skin cancer care. Selecting incomplete or inappropriate outcomes in HEE may under- or overestimate benefits for patients, misrepresent trade-offs between costs and outcomes and lead to suboptimal resource allocation. This comment discusses key considerations in selecting outcome measures for skin cancer HEE and proposes a conceptual decision matrix to guide meaningful outcome selection. Informed by methodological guidance and a targeted literature search, outcome domains applied in skin cancer HEE were categorized into quality of life based outcomes and clinical outcomes (hard and intermediate endpoints). These were assessed against predefined criteria covering measurement properties (e.g. sensitivity & validity), model integration (e.g. economic translatability & compatibility), decision relevance (e.g. interpretability for health technology assessment (HTA) & patient burden) and feasibility (e.g. data availability & generalizability). A qualitative scoring approach was used to make trade-offs between outcomes explicit. Generic quality of life outcomes facilitate comparability and interpretability for HTA but may lack sensitivity. Disease-specific outcomes better reflect patient-relevant domains but raise challenges regarding comparability. Intermediate clinical outcomes align closely with intervention mechanisms but require additional assumptions. The decision matrix highlights that no single outcome measure performs well across all criteria. Overall, the findings support selecting complementary outcome measures that reflect the intervention mechanisms, patient- preferences and burden and decision context. The proposed decision matrix provides a structured framework to support transparent and meaningful outcome selection.