OBJECTIVES: Mathematical models are required by decision makers to provide insight into pharmcoeconomic benefits associated with a product. It is therefore essential that manufacturers understand economic evidence requirements when submitting an application to a Health Technology Assessment (HTA) agency. METHODS: A literature search of economic recommendations from the following HTA agencies was conducted: CADTH (Canada), HAS (France), IQWiG (Germany), NICE (England), PBAC (Australia), PHARMAC (New Zealand) and SMC (Scotland). RESULTS: Cost-effectiveness analysis is considered the most relevant analytical technique across the English-speaking agencies with a preference for QALY-based analysis, in contrast to IQWiG which does not consider QALYs and utilities as central to their methods. Unlike other HTA agencies assessed, the French agency HAS does not currently require cost-effectiveness modelling in its decision-making process.  EQ-5D is the most commonly used utility instrument, NICE being the most prescriptive agency in this regard. However, utilities mapped from disease-specific quality of life measures may be accepted and agencies such as PBAC and IQWiG express no formal preference between instruments. The third-party payer is the most commonly required perspective adopted across the English-speaking agencies while IQWiG and PBAC recommend a societal perspective in addition to the payer’s perspective. This trend towards a broader, societal perspective may, however, be limited by uncertainties around measurement of wider costs. Sensitivity analyses are required by all agencies to explore uncertainty in the model. NICE and CADTH both favor a probabilistic approach while PBAC prefers univariate and multivariate analyses. Other agencies expect the manufacturer to justify their approach and choice. CONCLUSIONS: The recommendations of IQWiG and PBAC differ from the other selected agencies. Uniquely, in France the pharmaeconomic case is considered separately from the HTA process by the French Health Economists Association. These differences between agencies should be considered when planning evidence generation activities to support economic model development.