Vass CM, Boeri M, Poulos C, Turner AJ. Matching and weighting in health preference research. Presented at the 2021 Virtual 12th Meeting of the International Academy of Health Preference Research; June 2021. [abstract] Patient. 2021 Jun 18; p.1-6. doi: 10.1007/s40271-021-00532-0

BACKGROUND: There is a growing interest in quantifying the degree of heterogeneity in stated preferences for health. A popular investigation into preference heterogeneity involves split-sample analysis to make comparisons across subgroups. However, subgroups may differ in many observed characteristics. Not accounting for these other characteristics may bias comparisons if these are also associated with preferences. This study explores matching and weighting approaches to identify differences in preferences.

METHODS: We compare simulated stated preferences of patients and the public for a hypothetical healthcare intervention, where patients are older and have lower household income. The utility function for both is specified to be identical (preference homogeneity) and utility is assumed to increase with health and life years, and decrease with risk and cost. Utility for cost is specified as a function of income and age. We conduct unmatched, propensity score-matched, and entropy balanced analyses.

RESULTS: Due to differences in age and income, unmatched analysis detects statistically significant differences in the preference for cost when comparing the public’s preferences with those of patients. Both propensity score matching and entropy balancing reduce imbalance in the individual characteristics across subgroups, although the reduction is greater when using entropy balancing. Following matching or weighting, there are no significant differences in the preference weights for any attributes.

CONCLUSION: Unweighted and unmatched analyses may produce erroneous conclusions regarding heterogeneity in preferences when making comparisons across subgroups. Matching and weighting methods may be useful for researchers seeking to compare preferences for health and health care when there are too many characteristics to feasibly incorporate with interaction terms.

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