Brendan Mulhern1, Richard Norman2, Deborah J Street3, Rosalie Viney3. 1. Centre for Health Economics Research and Evaluation, University of Technology, 1-59 Quay St, Haymarket, Sydney, NSW, 2000, Australia. brendan.mulhern@chere.uts.edu.au. 2. School of Public Health, Curtin University, Kent Street, Bentley, Perth, WA, 6102, Australia. 3. Centre for Health Economics Research and Evaluation, University of Technology, 1-59 Quay St, Haymarket, Sydney, NSW, 2000, Australia.
Abstract
BACKGROUND: Discrete-choice experiments (DCEs) are used in the development of preference-based measure (PBM) value sets. There is considerable variation in the methodological approaches used to elicit preferences. OBJECTIVE: Our objective was to carry out a structured review of DCE methods used for health state valuation. METHODS: PubMed was searched until 31 May 2018 for published literature using DCEs for health state valuation. Search terms to describe DCEs, the process of valuation and preference-based instruments were developed. English language papers with any study population were included if they used DCEs to develop or directly inform the production of value sets for generic or condition-specific PBMs. Assessment of paper quality was guided by the recently developed Checklist for Reporting Valuation Studies. Data were extracted under six categories: general study information, choice task and study design, type of designed experiment, modelling and analysis methods, results and discussion. RESULTS: The literature search identified 1132 published papers, and 63 papers were included in the review. Paper quality was generally high. The study design and choice task formats varied considerably, and a wide range of modelling methods were employed to estimate value sets. CONCLUSIONS: This review of DCE methods used for developing value sets suggests some recurring limitations, areas of consensus and areas where further research is required. Methodological diversity means that the values should be seen as experimental, and users should understand the features of the value sets produced before applying them in decision making.
BACKGROUND: Discrete-choice experiments (DCEs) are used in the development of preference-based measure (PBM) value sets. There is considerable variation in the methodological approaches used to elicit preferences. OBJECTIVE: Our objective was to carry out a structured review of DCE methods used for health state valuation. METHODS: PubMed was searched until 31 May 2018 for published literature using DCEs for health state valuation. Search terms to describe DCEs, the process of valuation and preference-based instruments were developed. English language papers with any study population were included if they used DCEs to develop or directly inform the production of value sets for generic or condition-specific PBMs. Assessment of paper quality was guided by the recently developed Checklist for Reporting Valuation Studies. Data were extracted under six categories: general study information, choice task and study design, type of designed experiment, modelling and analysis methods, results and discussion. RESULTS: The literature search identified 1132 published papers, and 63 papers were included in the review. Paper quality was generally high. The study design and choice task formats varied considerably, and a wide range of modelling methods were employed to estimate value sets. CONCLUSIONS: This review of DCE methods used for developing value sets suggests some recurring limitations, areas of consensus and areas where further research is required. Methodological diversity means that the values should be seen as experimental, and users should understand the features of the value sets produced before applying them in decision making.
Authors: Richard Norman; Benjamin M Craig; Paul Hansen; Marcel F Jonker; John Rose; Deborah J Street; Brendan Mulhern Journal: Patient Date: 2019-06 Impact factor: 3.883
Authors: N Devlin; T Pan; S Kreimeier; J Verstraete; E Stolk; K Rand; M Herdman Journal: Health Qual Life Outcomes Date: 2022-07-06 Impact factor: 3.077
Authors: David J Mott; Koonal K Shah; Juan Manuel Ramos-Goñi; Nancy J Devlin; Oliver Rivero-Arias Journal: Med Decis Making Date: 2021-03-18 Impact factor: 2.583