How to identify and treat data inconsistencies when eliciting health-state utility values for patient-centered...

Health utilities express the perceptions patients have on the impact potential adverse events of medical treatments may have on their quality of life. Being able to accurately assess health utilities is crucial when deciding what is the best treatment when multiple and diverse treatment options exist, or when performing a cost / utility analysis. Due to the emotional and other complexities that may exist when such data are elicited, the values of the health utilities may be inaccurate and cause inconsistencies. Existing literature indicates that such inconsistencies may be very frequent. However, no method has been developed for dealing with such inconsistencies in an effective manner.

Given a set of health utilities, this paper first explores ways for determining if there are any inconsistencies in their values. It also proposes a number of quadratic optimization approaches to best estimate the actual (and hence unknown) values when a set of initial health utility values are provided by the patient and certain inconsistencies have been detected. This is achieved by readjusting the initial values in a way that is minimal and also satisfies certain consistency requirements.

The proposed methods are applied on an illustrative example related to localized prostate cancer. Data from some published studies were used to illustrate how a set of initial values can be analyzed. This analysis aims at readjusting them in a minimal manner that would also satisfy some key numerical constraints pertinent to health utility values.

The numerical results and the computational complexities of the proposed models indicate that the proposed approaches are practical as they involve quadratic optimization modeling. These approaches are novel as the problem of addressing numerical inconsistencies in the elicitation process of health utilities has not been addressed adequately. The approaches are also critical in shared decision making and also when performing cost / utility analyses because health utilities play a central role in determining the quality-adjusted life years when making decisions in these healthcare domains.