Estimating Joint Health State Utility Algorithms Under Partial Information

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Jeremy W. Bray, Professor and Department Head (Creator)
The University of North Carolina at Greensboro (UNCG )
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Abstract: Objectives We explored the performance of existing joint health state utility estimators when dataare not available on utilities that isolate single-condition health states excluding any co-occurringcondition.Methods Using data from the National Epidemiologic Survey on Alcohol and Related Conditions-III, we defined 2 information sets: (1) a full-information set that includes the narrowly definedhealth state utilities used in most studies that test the performance of joint health state utilityestimators, and (2) a limited information set that includes only the more broadly defined healthstate utilities more commonly available to researchers. We used an example of alcohol use disorderco-occurring with cirrhosis of the liver, depressive disorder, or nicotine use disorder to illustrateour analysis.Results We found that the performance of joint health state utility estimators is appreciablydifferent under limited information than under full information. Full-information estimatorstypically overestimate the joint state utility, whereas limited-information estimators underestimatethe joint state utility, except for the minimum estimator, which is overestimated in all cases.Conclusions Researchers using joint health state utility estimators should understand theinformation set available to them and use methodological guidance appropriate for thatinformation set. We recommend the minimum estimator under limited information based on itsease of use, consistency (and therefore a predictable direction of bias), and lower root meansquared error.

Additional Information

Journal of the International Society for Pharmacoeconomics and Outcomes Research, 26(5)
Language: English
Date: 2022
combining, comorbidity, quality of life, utility

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