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What Is a Valid Mapping Algorithm in Cost-Utility Analyses? A Response From a Missing Data Perspective.

Yasuhiro Hagiwara1, Takuya Kawahara2, Takeru Shiroiwa3

  • 1Department of Biostatistics, School of Public Health, The University of Tokyo, Tokyo, Japan.

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research
|September 17, 2020
PubMed
Summary
This summary is machine-generated.

Mapping algorithms for cost-utility analyses (CUAs) require specific conditions to ensure accurate health utility estimates. Violating these conditions, such as incomplete data overlap, can lead to biased results in CUAs.

Keywords:
health utilitymappingnon–preference-based measurepreference-based measuresimulation

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Area of Science:

  • Health Economics
  • Biostatistics
  • Pharmacoeconomics

Background:

  • Mapping algorithms are crucial for cost-utility analyses (CUAs), translating non-preference-based measures into health utility values.
  • However, the conditions under which these mapping algorithms perform reliably in CUAs remain poorly understood.
  • Existing methods often lack clear validation criteria for CUA applications.

Purpose of the Study:

  • To define the conditions necessary for a valid mapping algorithm specifically for CUA.
  • To investigate the impact of violated conditions on the performance of mapping algorithms in CUAs.
  • To establish a foundational framework for developing and selecting reliable mapping algorithms.

Main Methods:

  • Formulated the mapping problem as a missing data problem.
  • Defined a valid mapping algorithm tailored for CUA, distinct from predictive purposes.
  • Derived a sufficient set of conditions for algorithm validity.
  • Conducted a simulation study to assess algorithm performance under satisfied and violated conditions.

Main Results:

  • Complete overlap between the source measure and target health utility measure is critical.
  • Omission of relevant covariates not captured by the source measure can invalidate mapping algorithms.
  • Violation of any of the three derived conditions led to biased health utility estimates in simulations.
  • Prediction accuracy did not reliably indicate an algorithm's performance in CUA.

Conclusions:

  • The derived conditions provide essential criteria for developing and selecting robust mapping algorithms for CUAs.
  • Adherence to these conditions is fundamental for ensuring the validity and reliability of health utility estimates in economic evaluations.
  • This research offers a basis for improved practices in health economic modeling.