OBJECTIVES: Transferring cost-effectiveness information between geographic domains offers the potential for more efficient use of analytical resources. However, it is difficult for decision-makers to know when they can rely on cost-effectiveness evidence produced for another context.  This paper explores the transferability of economic evaluation results produced for one geographic area to another location of interest, and outlines an approach to identify factors to predict when this is appropriate. METHODS: We developed multilevel statistical models for the integration of published cost-effectiveness data to assess the impact of contextual effects on country level; whilst controlling for baseline characteristics within, and across, a set of economic evaluation studies. Explanatory variables were derived from a list of factors suggested in the literature as possible constraints on the transferability of cost-effectiveness evidence. We illustrated our approach using published estimates of the cost-effectiveness of statins for the primary and secondary prevention of cardiovascular disease (CVD). 2094 estimates of the costs and effects of statins were abstracted from 67 studies related to 23 geographic domains, together with covariates on data, study and country level. RESULTS: The proportion of variation at the country-level observed depends on the appropriate multilevel model structure and never exceeds 15% for incremental effects and 21% for incremental cost respectively. Key sources of variability are patient and disease characteristics, intervention cost and a number of methodological characteristics defined on the data level. There were fewer significant covariates on the study and country levels. CONCLUSIONS: Our analysis suggests that variability in cost-effectiveness data is primarily due to differences between studies, not countries. Further, comparing different models suggests that data from multinational studies severely underestimates country-level variability. Additional research is needed to test the robustness of these conclusions on other sets of cost-effectiveness data, and to further explore the appropriate set of covariates.