Accounting for Preference Heterogeneity in Discrete-Choice Experiments: An ISPOR Special Interest Group Report.

Journal Article (Journal Article;Systematic Review)

OBJECTIVES: Discrete choice experiments (DCEs) are increasingly used to elicit preferences for health and healthcare. Although many applications assume preferences are homogenous, there is a growing portfolio of methods to understand both explained (because of observed factors) and unexplained (latent) heterogeneity. Nevertheless, the selection of analytical methods can be challenging and little guidance is available. This study aimed to determine the state of practice in accounting for preference heterogeneity in the analysis of health-related DCEs, including the views and experiences of health preference researchers and an overview of the tools that are commonly used to elicit preferences. METHODS: An online survey was developed and distributed among health preference researchers and nonhealth method experts, and a systematic review of the DCE literature in health was undertaken to explore the analytical methods used and summarize trends. RESULTS: Most respondents (n = 59 of 70, 84%) agreed that accounting for preference heterogeneity provides a richer understanding of the data. Nevertheless, there was disagreement on how to account for heterogeneity; most (n = 60, 85%) stated that more guidance was needed. Notably, the majority (n = 41, 58%) raised concern about the increasing complexity of analytical methods. Of the 342 studies included in the review, half (n = 175, 51%) used a mixed logit with continuous distributions for the parameters, and a third (n = 110, 32%) used a latent class model. CONCLUSIONS: Although there is agreement about the importance of accounting for preference heterogeneity, there are noticeable disagreements and concerns about best practices, resulting in a clear need for further analytical guidance.

Full Text

Duke Authors

Cited Authors

  • Vass, C; Boeri, M; Karim, S; Marshall, D; Craig, B; Ho, K-A; Mott, D; Ngorsuraches, S; Badawy, SM; Mühlbacher, A; Gonzalez, JM; Heidenreich, S

Published Date

  • May 2022

Published In

Volume / Issue

  • 25 / 5

Start / End Page

  • 685 - 694

PubMed ID

  • 35500943

Electronic International Standard Serial Number (EISSN)

  • 1524-4733

Digital Object Identifier (DOI)

  • 10.1016/j.jval.2022.01.012


  • eng

Conference Location

  • United States