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Estimating Joint Health State Utility Algorithms Under Partial Information.

Jeremy W Bray1, Benjamin D Thornburg2, Abraham W Gebreselassie1

  • 1Department of Economics, UNC Greensboro, Greensboro, NC, USA.

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research
|October 28, 2022
PubMed
Summary
This summary is machine-generated.

Joint health state utility estimators perform differently based on available data. Limited information often leads to underestimation, with the minimum estimator recommended for its predictable bias and ease of use in health economics research.

Keywords:
combiningcomorbidityquality of lifeutility

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Area of Science:

  • Health Economics
  • Biostatistics
  • Epidemiology

Background:

  • Joint health state utility estimation is crucial for health economic evaluations.
  • Existing estimators often assume availability of narrowly defined single-condition utilities.
  • Limited data on utilities excluding co-occurring conditions presents a challenge.

Purpose of the Study:

  • To evaluate the performance of joint health state utility estimators with limited information.
  • To compare estimator behavior when only broadly defined utilities are available.
  • To provide guidance for researchers working with incomplete health state utility data.

Main Methods:

  • Utilized data from the National Epidemiologic Survey on Alcohol and Related Conditions-III.
  • Defined and compared full-information and limited-information sets for health state utilities.
  • Analyzed estimator performance using an example of co-occurring alcohol use disorder with other conditions.

Main Results:

  • Joint health state utility estimator performance differs significantly between full and limited information sets.
  • Full-information estimators tend to overestimate joint state utility.
  • Limited-information estimators generally underestimate utility, with the minimum estimator being an exception.

Conclusions:

  • Understanding the available information set is critical for selecting appropriate utility estimation methods.
  • The minimum estimator is recommended under limited information due to its consistency and lower root mean squared error.
  • Methodological guidance tailored to data availability is essential for accurate health economic modeling.