OBJECTIVES: To develop a quantitative process to estimate the probability of a favorable assessment of additional clinical benefit for a new drug for a chronic non-life threatening disease in Germany  METHODS: A multi-criteria decision process was use that included: selection of 7 German decision makers; pre-workshop questionnaires to identify the most important attributes for the decision and their relative importance; a workshop to develop levels for the most important attributes, map each attribute level to a value function and identify marginal drug profiles (drugs just demonstrating 'additional clinical benefit'); post-workshop estimation of weighted scores for each marginal product based on the estimated values and relative importance of their attribute levels and a logistic regression model to estimate the probability of an 'additional clinical benefit' decision as a function of the weighted scores; post-workshop questionnaire to validate the logistic regression model using participant ratings for some hypothetical products.  RESULTS: The most important attributes identified for a determination of 'additional clinical benefit' and their relative importance weights (%) were: robustness of clinical evidence (29%); incremental efficacy (19%); unmet need (12%); incremental impact on QOL (10%); availability of alternative treatments (9%); safety of new drug (9%); burden of disease (5%); availability of other country evaluations (4%); budget impact (3%). The attribute levels and relative value for a positive decision (0-1) for the most important attribute, robustness of clinical evidence, were: 'endpoints and/or comparators not relevant to patients' (0); 'clinical endpoints relevant but comparators not relevant needing indirect comparisons' (0.764); and 'all clinical endpoints and comparators relevant for patients and payers (1)'. The estimates of the probability of an 'additional clinical benefit' for the hypothetical products using the logistic regression model had 71% positive predictive value and 85% negative predictive value when compared to participant decisions for these hypothetical products. CONCLUSIONS: An MCDA process can provide both a qualitative understanding and quantitative estimates of the relative importance, attribute levels, and value scales of different product attributes that influence a decision of 'additional clinical benefit' in Germany.