Caroline Vass1, Marco Boeri2, Suzanna Karim3, Deborah Marshall4, Ben Craig4, Kerrie-Anne Ho5, David Mott6, Surachat Ngorsuraches7, Sherif M Badawy8, Axel Mühlbacher9, Juan Marcos Gonzalez10, Sebastian Heidenreich11. 1. RTI Health Solutions, Manchester, England, UK; Manchester Centre for Health Economics, The University of Manchester, Manchester, England, UK. 2. RTI Health Solutions, Belfast, Northern Ireland, UK; Queen's University Belfast, Belfast, Northern Ireland, UK. 3. University of South Florida, Tampa, FL, USA. 4. University of Calgary, Calgary, Canada. 5. UCB Pharma, Slough, England, UK. 6. Office of Health Economics, London, England, UK. 7. Auburn University, Auburn, AL, USA. 8. Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA; Division of Hematology, Oncology and Stem Cell Transplant, Lurie Children's Hospital of Chicago, Chicago, IL, USA. 9. Hochschule Neubrandenburg, Neubrandenburg, Germany; Duke Department of Population Health Sciences, Duke University, Durham, NC, USA; Center for Health Policy and Inequalities Research at the Duke Global Health Institute, Duke University, Durham, NC, USA. 10. Duke Clinical Research Institute, Duke University, Durham, NC, USA. 11. University of Calgary, Calgary, Canada; Evidera Inc, London, England, UK. Electronic address: sebastian.heidenreich@evidera.com.
Abstract
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.
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.
Authors: Suzana Karim; Benjamin M Craig; Caroline Vass; Catharina G M Groothuis-Oudshoorn Journal: Pharmacoeconomics Date: 2022-08-12 Impact factor: 4.558