Does meta-analysis of economic evaluations have the potential to play a role in healthcare decision-making in the United States?
Issued Date
2022-01-01
Resource Type
ISSN
13696998
eISSN
1941837X
Scopus ID
2-s2.0-85131702222
Pubmed ID
35621016
Journal Title
Journal of Medical Economics
Volume
25
Issue
1
Start Page
750
End Page
754
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Medical Economics Vol.25 No.1 (2022) , 750-754
Suggested Citation
Veettil S.K. Does meta-analysis of economic evaluations have the potential to play a role in healthcare decision-making in the United States?. Journal of Medical Economics Vol.25 No.1 (2022) , 750-754. 754. doi:10.1080/13696998.2022.2083347 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/86522
Title
Does meta-analysis of economic evaluations have the potential to play a role in healthcare decision-making in the United States?
Author(s)
Other Contributor(s)
Abstract
The current use of economic evidence in the decision-making process in the US is increasing. Meta-analysis of economic evaluations (MAEE) has gained recognition in recent years and can support decision-making at the global level or for countries with resource constraints. The focus of this article is to demonstrate how MAEE may contribute to the decision-making process in the US healthcare system. We demonstrated that MAEE can provide an efficient mechanism to quantitatively summarize cost-effectiveness findings based on all existing studies from the US answering the same question across different assumptions. This sort of evidence is important for US policymakers to support policy decision-making. MAEE methods can streamline the process of reviewing complex economic models and their findings, which has been previously reported by stakeholders as a barrier to the use of economic evidence in decision-making in the US. However, the currently proposed method may not fully address the issue of heterogeneity observed among MAEEs. There is a critical need to explore sources of heterogeneity and develop a standardized approach to handle it to improve the efficiency and acceptability of future MAEEs.