OBJECTIVES: Large translational research projects often have abstract objectives, such as reducing the burden of disease and healthcare costs from type-2 diabetes (T2DM). Such an abstract objective entails: i) a very large number of possible strategies to reach the objective and ii) a lack of detailed data and high levels in uncertainty. Currently, no methods to support project selection and resource allocation decisions in such a setting are available. METHODS: As a case study, we supported a resource allocation decision for the remaining funds in a large Dutch translational research consortium with the aforementioned objective, and compared the results to the decision made at the start of the project. We used the problem structuring, model building methods from multi-criteria decision analysis to identify four different alternative research strategies, and a set of evaluation criteria. Consequently, we used a combination of judgment from experts involved in the project and previously published data on the burden of disease and healthcare costs to evaluate the alternatives. Finally, a decision analysis was performed using Stochastic Multicriteria Acceptability Analysis for ordinal data (SMAA-O), which allows for the combined use of quantitative and qualitative (ranked) data.  RESULTS: Using our method, it was decided to allocate remaining resources to the identification of biomarkers and development of technologies that can be used in the prevention of macrovascular complications in T2DM patients. This decision differed from the one made at the start of the project, which was not supported by any formal decision analysis. CONCLUSIONS: Our study shows that our method using SMAA-O can be a practical and valuable tool to support decisions on the allocation of research funds within large translational research consortia.