An examination of machine learning to map non-preference based patient reported outcome measures to health state utility values.

ABSTRACT: Non-preference-based patient-reported outcome measures (PROMs) are popular in health outcomes research. These measures, however, cannot be used to estimate health state utilities, limiting their usefulness for economic evaluations. Mapping PROMs to a multi-attribute utility instrument is one solution. While mapping is commonly conducted using econometric techniques, failing to specify the complex interactions between variables may lead to inaccurate prediction of utilities, resulting in inaccurate estimates of cost-effectiveness and suboptimal funding decisions. These issues can be addressed using machine learning. This paper evaluates the use of machine learning as a mapping tool. We adopt a comprehensive approach to compare six machine learning techniques with eight econometric technique

SUBMITTER: Aghdaee M 

PROVIDER: S-EPMC9545032 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature