OBJECTIVES: The Institute for Quality and Efficiency in Health Care (IQWiG) in Germany evaluates benefits/harms and economic implications of medical interventions. For the purpose of cost-benefit analysis, IQWiG has developed the efficiency frontier concept to determine the maximum reimbursable price for pharmaceuticals. Within this concept benefits/harms are evaluated for each patient-relevant endpoint. Methodological problems arise with the presence of multiple patient-relevant endpoints because recommendations for the maximum reimbursable price will likely be imprecise. Conjoint analysis (CA) and analytic hierarchy process (AHP) are being discussed as potential approaches to aggregate multiple patient-relevant endpoints. The objective of this contribution was to describe both approaches and compare them with respect to their suitability as a method for aggregating multiple patient-relevant endpoints within IQWiG's efficiency frontier concept. METHODS: A catalogue with criteria has been established to assess both approaches with regard to their suitability for aggregating multiple patient-relevant endpoints. The catalogue comprises nine relevant legal and methodological aspects: two criteria were identified based on legal requirements; three criteria were included considering IQWiG method requirements; lastly, four general methodological requirements were considered. RESULTS: Both methods were assessed based on these criteria. Two criteria were identified that could be met by both CA and AHP. The remaining seven criteria could be met by either CA or AHP. None of IQWiG's proposed approaches for prioritizing and weighting multiple patient-relevant endpoints could demonstrate to fulfill all relevant criteria when assessed with regard to legal and methodological requirements. CONCLUSIONS: With the presence of multiple patient-relevant endpoints the implementation of the efficiency frontier concept remains unclear due to lack of methodological guidance on how to aggregate multiple endpoints. There is substantial need for further (empirical) research in methods for aggregating multiple patient-relevant endpoints.