Matching and weighting in stated preferences for health care. (September 2022)

• Record Type: Journal Article
• Authors: Vass, Caroline M.; Boeri, Marco; Poulos, Christine; Turner, Alex J.
• Is Part Of: Journal of choice modelling. Volume 44(2022)
• Publication Date: 2022-09
• Subjects: Discrete choice experiment -- Stated preferences -- Entropy balancing -- Propensity scores; Decision making -- Periodicals; Social choice -- Periodicals; Decision making; Social choice; Periodicals; 302.13
• DOI: 10.1016/j.jocm.2022.100367
• Languages: English
• ISSNs: 1755-5345

Abstract

Abstract: There is an increasing interest in the use of stated preference methods to understand individuals' preferences for health and healthcare. There is also a growing interest in understanding heterogeneity in individuals' preferences. Consequently, stated preference studies frequently consider models that capture either or both observed and unobserved preference heterogeneity. A popular preliminary investigation into heterogeneity involves split-sample analysis to compare subgroups' preferences e.g., comparing patients with clinicians, or older patients with younger. In fixed-effects models, the constant variables (the individuals' characteristics) remain stable across choice sets and therefore only enter the choice model when interacted with various attributes and/or levels. However, subgroups of respondents may differ on multiple variables that may not easily be implemented with interaction terms because of complexity and a lack of power thus only one, or a few, variables are typically taken into account in each subgroup model. This paper presents an overview of methods for matching and balancing samples to weight individuals with different characteristics in subgroup analysis and an example of how unweighted comparisons may produce erroneous conclusions regarding the degree of heterogeneity in preferences. We illustrate the issue with synthetic and empirical datasets to explore methods for matching subgroups before and within simple choice models. Our results show that entropy balancing and propensity score matching could be more appropriate than analyses using unmatched preference data when heterogeneity is driven by multiple factors. The paper concludes with a discussion of when matching and weighting may and may not be useful in healthcare decision-making. Highlights: Subgroup analysis to investigate preference heterogeneity is commonplace. Direct subgroup comparisons can be biased if other characteristics also influence preferences. Matching or weighting techniques can be used to ensure balance of multiple characteristics across subgroups. Whether these techniques are required depends on the research question of interest.