A Review of Methodological Considerations for Economic Evaluations of Gene Therapies and Their Application in Literature

Objectives: To identify methodological considerations discussed in literature addressing economic evaluations (EEs) of gene therapies (GTs). Additionally, we assessed if these considerations are applied in published GT EEs to increase understanding and explore impact. Methods: First a peer-reviewed literature review was performed to identify research addressing methodological considerations of GT EEs until August 2019. Identified considerations were grouped in themes using thematic content analysis. A second literature search was conducted in which we identified published evaluations. The EE quality of reporting was assessed using Consolidated Health Economic Evaluation Reporting Standards. Results: The first literature search yielded 13 articles discussing methodological considerations. The second search provided 12 EEs. Considerations identified were payment models, definition of perspectives, addressing uncertainty, data extrapolation, discount rates, novel value elements, and use of indirect and surrogate endpoints. All EEs scored satisfactory to good according to Consolidated Health Economic Evaluation Reporting Standards. Regarding methodological application, we found 1 methodological element (payment models) was applied in 2 base cases. Scenarios explored alternative perspectives, survival assumptions, and extrapolation methods in 10 EEs. Conclusions: Although EE quality of reporting was considered good, their informativeness for health technology assessment and decision makers seemed limited owing to many uncertainties. We suggest accepted EE methods can broadly be applied to GTs, but few elements may need adjustment. Further research and multi-stakeholder consensus is needed to determine appropriateness and application of individual methodological considerations. For now, we recommend including scenario analyses to explore impact of methodological choices and (clinical) uncertainties. This study contributes to better understanding of perceived appropriate evaluation of GTs and informs best modeling practices.