The importance of preference interactions in joint health-state utility values applied to decision analyses for shared decision-making

Abstract

The direct elicitation of health-state utility values (HSUVs) is difficult, and inconsistent HSUVs are a prevalent problem. Joint health conditions (JHCs) affect people’s quality of life in different ways. They can be preference substitutes, preference complements, or mutually utility independent. This article develops a novel model called the correlated bivariate Bernoulli health-utility (CBBHU) model, for estimating joint HSUVs (JHSUVs) from known single HSUVs and a small subset of elicited JHSUVs. A bivariate health utility function (HUF) is developed for the dependence of JHSUVs on severity. It consists of the product of the constituent single HUFs and a bivariate function with two parameters that vary with health conditions and patients’ preferences. These parameters can be fitted to as few as two severity levels and the parametrized HUF used to estimate HSUVs for different severity levels. A bootstrap method that requires a significantly reduced number of elicited HSUVs is proposed for estimating JHSUVs for three or more JHCs. The CBBHU functions satisfy the Fréchet bounds and provide internally consistent HSUVs. Preference interactions can have a substantial impact on patients’ medical decisions. CBBHU values are appropriate for shared decision-making applications.

HIGHLIGHTS

• The CBBHU model is a novel theoretical model for estimating JHSUVs.

• The CBBHU model provides a practical approach to model and predict reliable JHSUVs.

• The JHSUVs satisfy the Fréchet inequalities, utility theory, and prospect theory.

• The CBBHU models JHCs that are preference complements and preference substitutes.

• A practical bootstrap method extends the CBBHU model to multimorbidities.

Keywords:

• Fréchet bounds
• joint health-state utility values
• lottery-equivalence gamble
• preference interactions
• preference complements
• preference substitutes
• shared decision making

Acknowledgments

The author gratefully acknowledges Professor Maria Mayorga, Department Editor IISE Transactions on Healthcare Systems Engineering, and two anonymous referees for valuable suggestions.

Disclosure of interest

The author reports no conflict of interest.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.