Health Economic Evaluations (HEEs) play a crucial role in reimbursement decisions, particularly in the realm of healthcare technologies. However, these evaluations are often fraught with uncertainty, posing significant challenges to decision-makers. The assessment of uncertainty is a cornerstone of these evaluations, helping to gauge the robustness of results and the potential consequences of uncertainty related to decisions. Yet, there is currently no comprehensive overview of the methods used for uncertainty assessment in HEEs.
A recent review aimed to fill this gap, exploring the various methods used for uncertainty assessment in model-based HEEs. The review identified a total of 80 different methods for assessing uncertainty: 30 for identification, 28 for analysis, and 22 for communication. These methods cater to different sources of uncertainty, with most identification methods developed to assess related concepts like validity, model quality, and relevance.
This review aids analysts and decision-makers by guiding them to choose suitable uncertainty assessment techniques based on their specific needs and the overall assessment purpose. However, it also highlights the need for more precise terminology and guidance on using these methodologies.
In conclusion, assessing uncertainty is crucial in HEEs. The paper below offers a comprehensive review of the methods the industry is currently using. Nevertheless, additional work is necessary to both clarify language and provide direction on the application of these methodologies.
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