OBJECTIVES: The generation of evidence in the early development of a technology is a bottle neck for uncertainty in decision-making. Health economic modeling can support the assessment of early innovation, but complex modeling approaches are criticized for being “black boxes” which in turn reduce their acceptance in health policy. METHODS:  MCDA can address the gap about what matters to health care decision makers and data collection by explicitly structuring decision criteria. The combination of MCDA modeling and health economic modeling implies a potential for gaining efficiency. The simulation model of the intervention for stroke, the MSU, is analyzed regarding application of MCDA. RESULTS: : Experts and stakeholders can support the: (1) Definition of model factors e.g. outcome parameter, which is generally accepted (Barthel-Index for Stroke outcome). (2) Evidence collection e.g. is the data valid? (outcome of thrombolysis) (3) Model structure e.g. interrelation between input parameters (stroke incidence, risk factors and population). (4) Validation of a model e.g. reassessment of step 1-3. (5) Final analysis by using MCDA e.g. importance of economic vs. medical benefit. CONCLUSIONS: The methodological combination is advantageous because simulation modeling as well as MCDA have a similar sequence of events. The simulation output, which is commonly validated by technical and medical experts, can gain validity by the heterogeneous perspectives of participating stakeholders in the validation process. For example, this raises questions about priority and importance of certain outcome parameters like the thrombolysis rates vs. the independence of patients after 3 months. Finally, the combination of MCDA and simulation modeling contributes to a transparent analytic process and results in a more complex understanding of the technology.     This project is supported by the German Federal Ministry of Education and Research (BMBF) as part of the National Cluster of Excellence ‚Medical Technologies - Medical Valley EMN’ (Project grant No. 13EX1013B).