Abstract
Although numerous mapping algorithms from a non-preference-based measure to a target health utility measure have been developed and applied in cost-utility analyses (CUAs), conditions for a mapping algorithm to work well in a CUA are still unclear. In this research, we formulate the mapping problem as a missing data problem and clarify these conditions. We defined a valid mapping algorithm based on the purpose of mapping (ie, not for prediction but for CUA), and derived a sufficient set of conditions for a valid mapping algorithm. We also conducted a simulation study to investigate properties of a mapping algorithm under situations where the conditions are satisfied and violated. The derived sufficient conditions indicate that the complete overlap of the source measure with the target health utility measure is important and that a covariate that is omitted from a mapping algorithm but has an effect on the target health utility measure not captured by the source measure may invalidate a mapping algorithm. The conditions cannot be verified from data in a CUA but can be supported using external data. A simulation study showed that when at least 1 of the 3 conditions was violated, a mapping algorithm provided biased health utility estimates in a CUA, and that prediction accuracy did not necessarily reflect performance of a mapping algorithm in a CUA. The derived conditions provide a fundamental basis for better practices in developing and selecting a mapping algorithm.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.